• DOMAIN: Digital content and entertainment industry
• CONTEXT: The objective of this project is to build a text classification model that analyses the customer's sentiments based on their reviews in the IMDB database. The model uses a complex deep learning model to build an embedding layer followed by a classification algorithm to analyse the sentiment of the customers.
• DATA DESCRIPTION: The Dataset of 50,000 movie reviews from IMDB, labelled by sentiment (positive/negative). Reviews have been preprocessed, and each review is encoded as a sequence of word indexes (integers). For convenience, the words are indexed by their frequency in the dataset, meaning the for that has index 1 is the most frequent word. Use the first 20 words from each review to speed up training, using a max vocabulary size of 10,000. As a convention, "0" does not stand for a specific word, but instead is used to encode any unknown word.
• PROJECT OBJECTIVE: Build a sequential NLP classifier which can use input text parameters to determine the customer sentiments.
Steps and tasks: [ Total Score: 30 points]
Hint: - Use imdb.load_data() method
from google.colab import drive
drive.mount('/content/drive')
Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount("/content/drive", force_remount=True).
!pip install ipython-autotime
%load_ext autotime
Requirement already satisfied: ipython-autotime in /usr/local/lib/python3.7/dist-packages (0.3.1) Requirement already satisfied: ipython in /usr/local/lib/python3.7/dist-packages (from ipython-autotime) (5.5.0) Requirement already satisfied: setuptools>=18.5 in /usr/local/lib/python3.7/dist-packages (from ipython->ipython-autotime) (57.4.0) Requirement already satisfied: simplegeneric>0.8 in /usr/local/lib/python3.7/dist-packages (from ipython->ipython-autotime) (0.8.1) Requirement already satisfied: traitlets>=4.2 in /usr/local/lib/python3.7/dist-packages (from ipython->ipython-autotime) (5.1.1) Requirement already satisfied: decorator in /usr/local/lib/python3.7/dist-packages (from ipython->ipython-autotime) (4.4.2) Requirement already satisfied: pickleshare in /usr/local/lib/python3.7/dist-packages (from ipython->ipython-autotime) (0.7.5) Requirement already satisfied: prompt-toolkit<2.0.0,>=1.0.4 in /usr/local/lib/python3.7/dist-packages (from ipython->ipython-autotime) (1.0.18) Requirement already satisfied: pexpect in /usr/local/lib/python3.7/dist-packages (from ipython->ipython-autotime) (4.8.0) Requirement already satisfied: pygments in /usr/local/lib/python3.7/dist-packages (from ipython->ipython-autotime) (2.6.1) Requirement already satisfied: six>=1.9.0 in /usr/local/lib/python3.7/dist-packages (from prompt-toolkit<2.0.0,>=1.0.4->ipython->ipython-autotime) (1.15.0) Requirement already satisfied: wcwidth in /usr/local/lib/python3.7/dist-packages (from prompt-toolkit<2.0.0,>=1.0.4->ipython->ipython-autotime) (0.2.5) Requirement already satisfied: ptyprocess>=0.5 in /usr/local/lib/python3.7/dist-packages (from pexpect->ipython->ipython-autotime) (0.7.0) time: 147 µs (started: 2021-11-21 15:52:32 +00:00)
Importing required libraries and loading the inbuilt dataset from tensorflow as mentioned in the hint
import tensorflow as tf
import numpy as np
desired_vocab_size=10000
INDEX_FROM=3
(X_train, y_train), (X_test, y_test) = tf.keras.datasets.imdb.load_data(num_words=desired_vocab_size,skip_top = INDEX_FROM, seed=42)
time: 7.27 s (started: 2021-11-21 15:52:32 +00:00)
In the dataset provided, we shall split the dataset in 4 parts. Unseen data will be used for final comparison of prediction and actual value. Train data is used to train the model and validation dataset is used to validate the model. Test data is used to find the overal accuracy of the model.
X_unseen_data = X_test[24950:]
y_unseen_data = y_test[24950:]
X_train = np.concatenate((X_train, X_test[0:10000]),axis=0)
y_train = np.concatenate((y_train, y_test[0:10000]),axis=0)
X_val = X_test[10000:20000]
y_val = y_test[10000:20000]
X_test = X_test[20000:24950]
y_test = y_test[20000:24950]
time: 4.88 ms (started: 2021-11-21 15:52:40 +00:00)
Checking the data type and shape of the dataset
print("X train type : ", type(X_train), " X train shape : ", X_train.shape , "y train type : ", type(y_train), " y train shape : ", y_train.shape)
print("X val type : ", type(X_val), " X val shape : ", X_val.shape , "y val type : ", type(y_val), " y val shape : ", y_val.shape)
print("X test type : ", type(X_test), " X test shape : ", X_test.shape , "y test type : ", type(y_test), " y train shape : ", y_test.shape)
print("X unseen_data type : ", type(X_unseen_data), " X unseen_data shape : ", X_unseen_data.shape , "y unseen_data type : ", type(y_unseen_data), " y unseen_data shape : ", y_unseen_data.shape)
X train type : <class 'numpy.ndarray'> X train shape : (35000,) y train type : <class 'numpy.ndarray'> y train shape : (35000,) X val type : <class 'numpy.ndarray'> X val shape : (10000,) y val type : <class 'numpy.ndarray'> y val shape : (10000,) X test type : <class 'numpy.ndarray'> X test shape : (4950,) y test type : <class 'numpy.ndarray'> y train shape : (4950,) X unseen_data type : <class 'numpy.ndarray'> X unseen_data shape : (50,) y unseen_data type : <class 'numpy.ndarray'> y unseen_data shape : (50,) time: 5.59 ms (started: 2021-11-21 15:52:40 +00:00)
Check random record in the dataset
print("10th X_train data :: " , X_train[10])
print("10th y_train data (label) :: " , y_train[10])
10th X_train data :: [2, 24, 31, 7, 6813, 118, 2048, 14, 16, 382, 6, 2, 589, 8, 1060, 309, 23, 4, 223, 29, 1021, 83, 4, 2, 6, 6713, 631, 1101, 2, 5, 6, 201, 7, 2, 832, 5325, 83, 6, 1349, 19, 6, 1058, 2531, 255, 37, 367, 19, 41, 336, 5, 1094, 1694, 90, 2, 366, 36, 104, 29, 203, 30, 35, 9077, 8, 6, 3200, 4074, 8681, 7138, 133, 1994, 26, 270, 56, 5, 21, 18, 4, 293, 1854, 8, 990, 125, 10, 10, 14, 2, 346, 127, 28, 33, 222, 107, 147, 919, 2, 4519, 1587, 187, 27, 583, 7, 3068, 34, 772, 4, 2, 23, 4, 1513, 5, 4, 1121, 5485, 7, 27, 5374, 223, 8, 1227, 721, 4, 3549, 8, 27, 7479, 4, 85, 9, 6, 223, 4883, 19, 4, 436, 604, 1364, 2, 8, 4, 1873, 17, 4, 2, 7, 4, 370, 1978, 94, 2, 10, 10, 4, 2, 3640, 2, 100, 28, 77, 4, 2, 18, 6, 2557, 201, 7, 1994, 2171, 725, 1994, 21, 3003, 346, 18, 32, 4, 2, 7, 943, 10, 10, 2, 2492, 9, 6364, 5, 788, 5, 4, 603, 9, 31, 18, 1799, 3654, 9397, 5, 481, 302, 24, 31, 7, 2, 118, 2048, 21, 290, 6, 171, 919] 10th y_train data (label) :: 0 time: 1.2 ms (started: 2021-11-21 15:52:40 +00:00)
Get indices for words from the IMDB dataset and print random train data.
word_to_id = tf.keras.datasets.imdb.get_word_index()
word_to_id = {k:(v+INDEX_FROM) for k,v in word_to_id.items()}
word_to_id["<PAD>"] = 0
word_to_id["<START>"] = 1
word_to_id["<UNK>"] = 2
word_to_id["<UNUSED>"] = 3
id_to_word = {value:key for key,value in word_to_id.items()}
print(' '.join(id_to_word[id] for id in X_train[1542] ))
<UNK> i have a feeling that the <UNK> bros depression era musicals are going to become a lot more <UNK> in the next couple of years yes we are in the economic <UNK> or have you been living under a rock and times look bleak but we always have the movies as a way to escape our troubles in the 30's film going was hugely popular even at the height of economic <UNK> <UNK> parade 1933 was one such film that audiences <UNK> to while this lloyd bacon directed musical doesn't quite capture the social issues of the time as gold <UNK> of 1933 1933 does it's still a wonderful showcase of talent we have to wait until the end of the film for the three <UNK> busby <UNK> <UNK> numbers but boy are they worth waiting for me yes little ruby keeler is a terrible singer and actress and her <UNK> is so so but <UNK> magical by a <UNK> <UNK> her and what seems to be a hundred other chorus girls into a <UNK> water wonderland of course <UNK> numbers could never really be performed on a stage they defy limits of gravity for one thing and they contrast <UNK> with the realism of the tough <UNK> non musical scenes and <UNK> also has james cagney in at the one of his all too few musicals really what couldn't this man do he even gets to take over from the leading man don sailor <UNK> and <UNK> over <UNK> shanghai <UNK> who is really little ruby in china girl wig he co stars with joan blondell his adorable <UNK> secretary who cagney somehow <UNK> in favour of other women until the final reel that is apparently blondell was the only other woman who cagney loved apart from his wife and you can see the mutual <UNK> in every scene time: 119 ms (started: 2021-11-21 15:52:40 +00:00)
printing first 5000 word in the word_to_id items
print(word_to_id.items())
ii=0
for word, i in sorted(word_to_id.items(),key=lambda x:x[1]):
if(ii<5000):
print(word, " :: ", i)
ii=ii+1
Streaming output truncated to the last 5000 lines.
<START> :: 1
<UNK> :: 2
<UNUSED> :: 3
the :: 4
and :: 5
a :: 6
of :: 7
to :: 8
is :: 9
br :: 10
in :: 11
it :: 12
i :: 13
this :: 14
that :: 15
was :: 16
as :: 17
for :: 18
with :: 19
movie :: 20
but :: 21
film :: 22
on :: 23
not :: 24
you :: 25
are :: 26
his :: 27
have :: 28
he :: 29
be :: 30
one :: 31
all :: 32
at :: 33
by :: 34
an :: 35
they :: 36
who :: 37
so :: 38
from :: 39
like :: 40
her :: 41
or :: 42
just :: 43
about :: 44
it's :: 45
out :: 46
has :: 47
if :: 48
some :: 49
there :: 50
what :: 51
good :: 52
more :: 53
when :: 54
very :: 55
up :: 56
no :: 57
time :: 58
she :: 59
even :: 60
my :: 61
would :: 62
which :: 63
only :: 64
story :: 65
really :: 66
see :: 67
their :: 68
had :: 69
can :: 70
were :: 71
me :: 72
well :: 73
than :: 74
we :: 75
much :: 76
been :: 77
bad :: 78
get :: 79
will :: 80
do :: 81
also :: 82
into :: 83
people :: 84
other :: 85
first :: 86
great :: 87
because :: 88
how :: 89
him :: 90
most :: 91
don't :: 92
made :: 93
its :: 94
then :: 95
way :: 96
make :: 97
them :: 98
too :: 99
could :: 100
any :: 101
movies :: 102
after :: 103
think :: 104
characters :: 105
watch :: 106
two :: 107
films :: 108
character :: 109
seen :: 110
many :: 111
being :: 112
life :: 113
plot :: 114
never :: 115
acting :: 116
little :: 117
best :: 118
love :: 119
over :: 120
where :: 121
did :: 122
show :: 123
know :: 124
off :: 125
ever :: 126
does :: 127
better :: 128
your :: 129
end :: 130
still :: 131
man :: 132
here :: 133
these :: 134
say :: 135
scene :: 136
while :: 137
why :: 138
scenes :: 139
go :: 140
such :: 141
something :: 142
through :: 143
should :: 144
back :: 145
i'm :: 146
real :: 147
those :: 148
watching :: 149
now :: 150
though :: 151
doesn't :: 152
years :: 153
old :: 154
thing :: 155
actors :: 156
work :: 157
10 :: 158
before :: 159
another :: 160
didn't :: 161
new :: 162
funny :: 163
nothing :: 164
actually :: 165
makes :: 166
director :: 167
look :: 168
find :: 169
going :: 170
few :: 171
same :: 172
part :: 173
again :: 174
every :: 175
lot :: 176
cast :: 177
us :: 178
quite :: 179
down :: 180
want :: 181
world :: 182
things :: 183
pretty :: 184
young :: 185
seems :: 186
around :: 187
got :: 188
horror :: 189
however :: 190
can't :: 191
fact :: 192
take :: 193
big :: 194
enough :: 195
long :: 196
thought :: 197
that's :: 198
both :: 199
between :: 200
series :: 201
give :: 202
may :: 203
original :: 204
own :: 205
action :: 206
i've :: 207
right :: 208
without :: 209
always :: 210
times :: 211
comedy :: 212
point :: 213
gets :: 214
must :: 215
come :: 216
role :: 217
isn't :: 218
saw :: 219
almost :: 220
interesting :: 221
least :: 222
family :: 223
done :: 224
there's :: 225
whole :: 226
bit :: 227
music :: 228
script :: 229
far :: 230
making :: 231
guy :: 232
anything :: 233
minutes :: 234
feel :: 235
last :: 236
since :: 237
might :: 238
performance :: 239
he's :: 240
2 :: 241
probably :: 242
kind :: 243
am :: 244
away :: 245
yet :: 246
rather :: 247
tv :: 248
worst :: 249
girl :: 250
day :: 251
sure :: 252
fun :: 253
hard :: 254
woman :: 255
played :: 256
each :: 257
found :: 258
anyone :: 259
having :: 260
although :: 261
especially :: 262
our :: 263
believe :: 264
course :: 265
comes :: 266
looking :: 267
screen :: 268
trying :: 269
set :: 270
goes :: 271
looks :: 272
place :: 273
book :: 274
different :: 275
put :: 276
ending :: 277
money :: 278
maybe :: 279
once :: 280
sense :: 281
reason :: 282
true :: 283
actor :: 284
everything :: 285
wasn't :: 286
shows :: 287
dvd :: 288
three :: 289
worth :: 290
year :: 291
job :: 292
main :: 293
someone :: 294
together :: 295
watched :: 296
play :: 297
american :: 298
plays :: 299
1 :: 300
said :: 301
effects :: 302
later :: 303
takes :: 304
instead :: 305
seem :: 306
beautiful :: 307
john :: 308
himself :: 309
version :: 310
audience :: 311
high :: 312
house :: 313
night :: 314
during :: 315
everyone :: 316
left :: 317
special :: 318
seeing :: 319
half :: 320
excellent :: 321
wife :: 322
star :: 323
shot :: 324
war :: 325
idea :: 326
nice :: 327
black :: 328
less :: 329
mind :: 330
simply :: 331
read :: 332
second :: 333
else :: 334
you're :: 335
father :: 336
fan :: 337
poor :: 338
help :: 339
completely :: 340
death :: 341
3 :: 342
used :: 343
home :: 344
either :: 345
short :: 346
line :: 347
given :: 348
men :: 349
top :: 350
dead :: 351
budget :: 352
try :: 353
performances :: 354
wrong :: 355
classic :: 356
boring :: 357
enjoy :: 358
need :: 359
rest :: 360
use :: 361
kids :: 362
hollywood :: 363
low :: 364
production :: 365
until :: 366
along :: 367
full :: 368
friends :: 369
camera :: 370
truly :: 371
women :: 372
awful :: 373
video :: 374
next :: 375
tell :: 376
remember :: 377
couple :: 378
stupid :: 379
start :: 380
stars :: 381
perhaps :: 382
sex :: 383
mean :: 384
came :: 385
recommend :: 386
let :: 387
moments :: 388
wonderful :: 389
episode :: 390
understand :: 391
small :: 392
face :: 393
terrible :: 394
playing :: 395
school :: 396
getting :: 397
written :: 398
doing :: 399
often :: 400
keep :: 401
early :: 402
name :: 403
perfect :: 404
style :: 405
human :: 406
definitely :: 407
gives :: 408
others :: 409
itself :: 410
lines :: 411
live :: 412
become :: 413
dialogue :: 414
person :: 415
lost :: 416
finally :: 417
piece :: 418
head :: 419
case :: 420
felt :: 421
yes :: 422
liked :: 423
supposed :: 424
title :: 425
couldn't :: 426
absolutely :: 427
white :: 428
against :: 429
boy :: 430
picture :: 431
sort :: 432
worse :: 433
certainly :: 434
went :: 435
entire :: 436
waste :: 437
cinema :: 438
problem :: 439
hope :: 440
entertaining :: 441
she's :: 442
mr :: 443
overall :: 444
evil :: 445
called :: 446
loved :: 447
based :: 448
oh :: 449
several :: 450
fans :: 451
mother :: 452
drama :: 453
beginning :: 454
killer :: 455
lives :: 456
5 :: 457
direction :: 458
care :: 459
already :: 460
becomes :: 461
laugh :: 462
example :: 463
friend :: 464
dark :: 465
despite :: 466
under :: 467
seemed :: 468
throughout :: 469
4 :: 470
turn :: 471
unfortunately :: 472
wanted :: 473
i'd :: 474
:: 475
children :: 476
final :: 477
fine :: 478
history :: 479
amazing :: 480
sound :: 481
guess :: 482
heart :: 483
totally :: 484
lead :: 485
humor :: 486
writing :: 487
michael :: 488
quality :: 489
you'll :: 490
close :: 491
son :: 492
guys :: 493
wants :: 494
works :: 495
behind :: 496
tries :: 497
art :: 498
side :: 499
game :: 500
past :: 501
able :: 502
b :: 503
days :: 504
turns :: 505
child :: 506
they're :: 507
hand :: 508
flick :: 509
enjoyed :: 510
act :: 511
genre :: 512
town :: 513
favorite :: 514
soon :: 515
kill :: 516
starts :: 517
sometimes :: 518
car :: 519
gave :: 520
run :: 521
late :: 522
eyes :: 523
actress :: 524
etc :: 525
directed :: 526
horrible :: 527
won't :: 528
viewer :: 529
brilliant :: 530
parts :: 531
self :: 532
themselves :: 533
hour :: 534
expect :: 535
thinking :: 536
stories :: 537
stuff :: 538
girls :: 539
obviously :: 540
blood :: 541
decent :: 542
city :: 543
voice :: 544
highly :: 545
myself :: 546
feeling :: 547
fight :: 548
except :: 549
slow :: 550
matter :: 551
type :: 552
anyway :: 553
kid :: 554
roles :: 555
killed :: 556
heard :: 557
god :: 558
age :: 559
says :: 560
moment :: 561
took :: 562
leave :: 563
writer :: 564
strong :: 565
cannot :: 566
violence :: 567
police :: 568
hit :: 569
stop :: 570
happens :: 571
particularly :: 572
known :: 573
involved :: 574
happened :: 575
extremely :: 576
daughter :: 577
obvious :: 578
told :: 579
chance :: 580
living :: 581
coming :: 582
lack :: 583
alone :: 584
experience :: 585
wouldn't :: 586
including :: 587
murder :: 588
attempt :: 589
s :: 590
please :: 591
james :: 592
happen :: 593
wonder :: 594
crap :: 595
ago :: 596
brother :: 597
film's :: 598
gore :: 599
none :: 600
complete :: 601
interest :: 602
score :: 603
group :: 604
cut :: 605
simple :: 606
save :: 607
ok :: 608
hell :: 609
looked :: 610
career :: 611
number :: 612
song :: 613
possible :: 614
seriously :: 615
annoying :: 616
shown :: 617
exactly :: 618
sad :: 619
running :: 620
musical :: 621
serious :: 622
taken :: 623
yourself :: 624
whose :: 625
released :: 626
cinematography :: 627
david :: 628
scary :: 629
ends :: 630
english :: 631
hero :: 632
usually :: 633
hours :: 634
reality :: 635
opening :: 636
i'll :: 637
across :: 638
today :: 639
jokes :: 640
light :: 641
hilarious :: 642
somewhat :: 643
usual :: 644
started :: 645
cool :: 646
ridiculous :: 647
body :: 648
relationship :: 649
view :: 650
level :: 651
opinion :: 652
change :: 653
happy :: 654
middle :: 655
taking :: 656
wish :: 657
husband :: 658
finds :: 659
saying :: 660
order :: 661
talking :: 662
ones :: 663
documentary :: 664
shots :: 665
huge :: 666
novel :: 667
female :: 668
mostly :: 669
robert :: 670
power :: 671
episodes :: 672
room :: 673
important :: 674
rating :: 675
talent :: 676
five :: 677
major :: 678
turned :: 679
strange :: 680
word :: 681
modern :: 682
call :: 683
apparently :: 684
disappointed :: 685
single :: 686
events :: 687
due :: 688
four :: 689
songs :: 690
basically :: 691
attention :: 692
7 :: 693
knows :: 694
clearly :: 695
supporting :: 696
knew :: 697
british :: 698
television :: 699
comic :: 700
non :: 701
fast :: 702
earth :: 703
country :: 704
future :: 705
cheap :: 706
class :: 707
thriller :: 708
8 :: 709
silly :: 710
king :: 711
problems :: 712
aren't :: 713
easily :: 714
words :: 715
tells :: 716
miss :: 717
jack :: 718
local :: 719
sequence :: 720
bring :: 721
entertainment :: 722
paul :: 723
beyond :: 724
upon :: 725
whether :: 726
predictable :: 727
moving :: 728
similar :: 729
straight :: 730
romantic :: 731
sets :: 732
review :: 733
falls :: 734
oscar :: 735
mystery :: 736
enjoyable :: 737
needs :: 738
appears :: 739
talk :: 740
rock :: 741
george :: 742
giving :: 743
eye :: 744
richard :: 745
within :: 746
ten :: 747
animation :: 748
message :: 749
theater :: 750
near :: 751
above :: 752
dull :: 753
nearly :: 754
sequel :: 755
theme :: 756
points :: 757
' :: 758
stand :: 759
mention :: 760
lady :: 761
bunch :: 762
add :: 763
feels :: 764
herself :: 765
release :: 766
red :: 767
team :: 768
storyline :: 769
surprised :: 770
ways :: 771
using :: 772
named :: 773
haven't :: 774
lots :: 775
easy :: 776
fantastic :: 777
begins :: 778
actual :: 779
working :: 780
effort :: 781
york :: 782
die :: 783
hate :: 784
french :: 785
minute :: 786
tale :: 787
clear :: 788
stay :: 789
9 :: 790
elements :: 791
feature :: 792
among :: 793
follow :: 794
comments :: 795
re :: 796
viewers :: 797
avoid :: 798
sister :: 799
showing :: 800
typical :: 801
editing :: 802
what's :: 803
famous :: 804
tried :: 805
sorry :: 806
dialog :: 807
check :: 808
fall :: 809
period :: 810
season :: 811
form :: 812
certain :: 813
filmed :: 814
weak :: 815
soundtrack :: 816
means :: 817
buy :: 818
material :: 819
somehow :: 820
realistic :: 821
figure :: 822
crime :: 823
doubt :: 824
gone :: 825
peter :: 826
tom :: 827
kept :: 828
viewing :: 829
t :: 830
general :: 831
leads :: 832
greatest :: 833
space :: 834
lame :: 835
suspense :: 836
dance :: 837
imagine :: 838
brought :: 839
third :: 840
atmosphere :: 841
hear :: 842
particular :: 843
sequences :: 844
whatever :: 845
parents :: 846
move :: 847
lee :: 848
indeed :: 849
learn :: 850
rent :: 851
de :: 852
eventually :: 853
note :: 854
deal :: 855
average :: 856
reviews :: 857
wait :: 858
forget :: 859
japanese :: 860
sexual :: 861
poorly :: 862
premise :: 863
okay :: 864
zombie :: 865
surprise :: 866
believable :: 867
stage :: 868
possibly :: 869
sit :: 870
who's :: 871
decided :: 872
expected :: 873
you've :: 874
subject :: 875
nature :: 876
became :: 877
difficult :: 878
free :: 879
killing :: 880
screenplay :: 881
truth :: 882
romance :: 883
dr :: 884
nor :: 885
reading :: 886
needed :: 887
question :: 888
leaves :: 889
street :: 890
20 :: 891
meets :: 892
hot :: 893
unless :: 894
begin :: 895
baby :: 896
superb :: 897
credits :: 898
imdb :: 899
otherwise :: 900
write :: 901
shame :: 902
let's :: 903
situation :: 904
dramatic :: 905
memorable :: 906
directors :: 907
earlier :: 908
meet :: 909
disney :: 910
open :: 911
dog :: 912
badly :: 913
joe :: 914
male :: 915
weird :: 916
acted :: 917
forced :: 918
laughs :: 919
sci :: 920
emotional :: 921
older :: 922
realize :: 923
fi :: 924
dream :: 925
society :: 926
writers :: 927
interested :: 928
footage :: 929
forward :: 930
comment :: 931
crazy :: 932
deep :: 933
sounds :: 934
plus :: 935
beauty :: 936
whom :: 937
america :: 938
fantasy :: 939
directing :: 940
keeps :: 941
ask :: 942
development :: 943
features :: 944
air :: 945
quickly :: 946
mess :: 947
creepy :: 948
towards :: 949
perfectly :: 950
mark :: 951
worked :: 952
box :: 953
cheesy :: 954
unique :: 955
setting :: 956
hands :: 957
plenty :: 958
result :: 959
previous :: 960
brings :: 961
effect :: 962
e :: 963
total :: 964
personal :: 965
incredibly :: 966
rate :: 967
fire :: 968
monster :: 969
business :: 970
leading :: 971
apart :: 972
casting :: 973
admit :: 974
joke :: 975
powerful :: 976
appear :: 977
background :: 978
telling :: 979
girlfriend :: 980
meant :: 981
christmas :: 982
hardly :: 983
present :: 984
battle :: 985
potential :: 986
create :: 987
bill :: 988
break :: 989
pay :: 990
masterpiece :: 991
gay :: 992
political :: 993
return :: 994
dumb :: 995
fails :: 996
fighting :: 997
various :: 998
era :: 999
portrayed :: 1000
co :: 1001
cop :: 1002
secret :: 1003
inside :: 1004
outside :: 1005
nudity :: 1006
reasons :: 1007
ideas :: 1008
twist :: 1009
western :: 1010
front :: 1011
missing :: 1012
boys :: 1013
match :: 1014
deserves :: 1015
jane :: 1016
expecting :: 1017
fairly :: 1018
villain :: 1019
talented :: 1020
married :: 1021
ben :: 1022
success :: 1023
william :: 1024
unlike :: 1025
rich :: 1026
attempts :: 1027
spoilers :: 1028
list :: 1029
manages :: 1030
social :: 1031
odd :: 1032
recently :: 1033
remake :: 1034
flat :: 1035
cute :: 1036
further :: 1037
sadly :: 1038
copy :: 1039
wrote :: 1040
agree :: 1041
doctor :: 1042
cold :: 1043
plain :: 1044
following :: 1045
mentioned :: 1046
sweet :: 1047
incredible :: 1048
missed :: 1049
pure :: 1050
crew :: 1051
office :: 1052
wasted :: 1053
ended :: 1054
produced :: 1055
gun :: 1056
filmmakers :: 1057
large :: 1058
caught :: 1059
revenge :: 1060
filled :: 1061
pace :: 1062
popular :: 1063
waiting :: 1064
'the :: 1065
members :: 1066
science :: 1067
decides :: 1068
considering :: 1069
hold :: 1070
public :: 1071
cartoon :: 1072
party :: 1073
tension :: 1074
created :: 1075
slightly :: 1076
uses :: 1077
convincing :: 1078
compared :: 1079
la :: 1080
familiar :: 1081
neither :: 1082
mary :: 1083
spent :: 1084
sees :: 1085
6 :: 1086
suddenly :: 1087
30 :: 1088
intelligent :: 1089
escape :: 1090
scott :: 1091
fear :: 1092
water :: 1093
brothers :: 1094
d :: 1095
clever :: 1096
entirely :: 1097
kills :: 1098
choice :: 1099
bored :: 1100
language :: 1101
moves :: 1102
spirit :: 1103
laughing :: 1104
dancing :: 1105
we're :: 1106
value :: 1107
cover :: 1108
credit :: 1109
state :: 1110
island :: 1111
successful :: 1112
trouble :: 1113
visual :: 1114
violent :: 1115
ultimately :: 1116
century :: 1117
singing :: 1118
15 :: 1119
concept :: 1120
basic :: 1121
italian :: 1122
positive :: 1123
german :: 1124
animated :: 1125
biggest :: 1126
exciting :: 1127
speak :: 1128
runs :: 1129
store :: 1130
died :: 1131
cat :: 1132
consider :: 1133
effective :: 1134
walk :: 1135
recent :: 1136
depth :: 1137
former :: 1138
amusing :: 1139
control :: 1140
common :: 1141
spend :: 1142
band :: 1143
appreciate :: 1144
zombies :: 1145
portrayal :: 1146
force :: 1147
c :: 1148
pointless :: 1149
rated :: 1150
books :: 1151
focus :: 1152
hair :: 1153
adventure :: 1154
younger :: 1155
solid :: 1156
trash :: 1157
adult :: 1158
impressive :: 1159
follows :: 1160
respect :: 1161
bizarre :: 1162
tone :: 1163
law :: 1164
super :: 1165
amount :: 1166
impossible :: 1167
mad :: 1168
company :: 1169
college :: 1170
van :: 1171
prison :: 1172
weren't :: 1173
conclusion :: 1174
chemistry :: 1175
win :: 1176
showed :: 1177
recommended :: 1178
slasher :: 1179
producers :: 1180
culture :: 1181
studio :: 1182
fit :: 1183
starring :: 1184
heavy :: 1185
situations :: 1186
project :: 1187
makers :: 1188
trip :: 1189
awesome :: 1190
accent :: 1191
considered :: 1192
disturbing :: 1193
changed :: 1194
sick :: 1195
failed :: 1196
decide :: 1197
somewhere :: 1198
won :: 1199
leaving :: 1200
barely :: 1201
honest :: 1202
cause :: 1203
questions :: 1204
shooting :: 1205
u :: 1206
longer :: 1207
post :: 1208
f :: 1209
anti :: 1210
tough :: 1211
aside :: 1212
ghost :: 1213
fake :: 1214
cult :: 1215
thanks :: 1216
meaning :: 1217
images :: 1218
fiction :: 1219
charming :: 1220
audiences :: 1221
computer :: 1222
tony :: 1223
brain :: 1224
planet :: 1225
south :: 1226
literally :: 1227
generally :: 1228
touch :: 1229
steve :: 1230
stick :: 1231
likes :: 1232
ex :: 1233
values :: 1234
pathetic :: 1235
magic :: 1236
involving :: 1237
surprisingly :: 1238
alive :: 1239
jim :: 1240
immediately :: 1241
grade :: 1242
yeah :: 1243
garbage :: 1244
100 :: 1245
dad :: 1246
bought :: 1247
military :: 1248
natural :: 1249
camp :: 1250
aspect :: 1251
honestly :: 1252
adaptation :: 1253
utterly :: 1254
detective :: 1255
ability :: 1256
fair :: 1257
shoot :: 1258
smith :: 1259
explain :: 1260
pick :: 1261
genius :: 1262
west :: 1263
glad :: 1264
frank :: 1265
sitting :: 1266
appearance :: 1267
pictures :: 1268
week :: 1269
motion :: 1270
appeal :: 1271
army :: 1272
standard :: 1273
attack :: 1274
knowing :: 1275
personally :: 1276
catch :: 1277
drive :: 1278
sexy :: 1279
normal :: 1280
rare :: 1281
nowhere :: 1282
added :: 1283
sam :: 1284
humour :: 1285
walking :: 1286
remains :: 1287
purpose :: 1288
edge :: 1289
comedies :: 1290
thinks :: 1291
loud :: 1292
beautifully :: 1293
thank :: 1294
silent :: 1295
taste :: 1296
unbelievable :: 1297
naked :: 1298
twists :: 1299
master :: 1300
touching :: 1301
subtle :: 1302
terms :: 1303
date :: 1304
equally :: 1305
dreams :: 1306
terrific :: 1307
channel :: 1308
drawn :: 1309
mood :: 1310
journey :: 1311
door :: 1312
chase :: 1313
fully :: 1314
complex :: 1315
london :: 1316
key :: 1317
wow :: 1318
managed :: 1319
road :: 1320
narrative :: 1321
laughable :: 1322
mistake :: 1323
bottom :: 1324
producer :: 1325
themes :: 1326
movie's :: 1327
pieces :: 1328
likely :: 1329
climax :: 1330
g :: 1331
disappointing :: 1332
club :: 1333
lovely :: 1334
harry :: 1335
blue :: 1336
nobody :: 1337
excuse :: 1338
outstanding :: 1339
soldiers :: 1340
issues :: 1341
stewart :: 1342
constantly :: 1343
award :: 1344
pass :: 1345
thus :: 1346
plan :: 1347
surely :: 1348
marriage :: 1349
painful :: 1350
justice :: 1351
costumes :: 1352
presented :: 1353
batman :: 1354
80's :: 1355
innocent :: 1356
soul :: 1357
wild :: 1358
noir :: 1359
cinematic :: 1360
spoiler :: 1361
vampire :: 1362
finish :: 1363
slowly :: 1364
ride :: 1365
gang :: 1366
contains :: 1367
christopher :: 1368
presence :: 1369
places :: 1370
besides :: 1371
government :: 1372
details :: 1373
train :: 1374
central :: 1375
thrown :: 1376
manner :: 1377
chris :: 1378
historical :: 1379
stunning :: 1380
photography :: 1381
charm :: 1382
hoping :: 1383
impression :: 1384
scenery :: 1385
speaking :: 1386
disappointment :: 1387
loves :: 1388
animals :: 1389
you'd :: 1390
developed :: 1391
drug :: 1392
smart :: 1393
charles :: 1394
indian :: 1395
numbers :: 1396
mysterious :: 1397
expectations :: 1398
color :: 1399
hey :: 1400
exception :: 1401
throw :: 1402
minor :: 1403
ahead :: 1404
double :: 1405
track :: 1406
stands :: 1407
suppose :: 1408
aspects :: 1409
boss :: 1410
woods :: 1411
sent :: 1412
festival :: 1413
bother :: 1414
cry :: 1415
church :: 1416
feelings :: 1417
critics :: 1418
green :: 1419
brief :: 1420
acts :: 1421
opera :: 1422
filming :: 1423
mainly :: 1424
support :: 1425
emotion :: 1426
element :: 1427
held :: 1428
fascinating :: 1429
building :: 1430
million :: 1431
boyfriend :: 1432
names :: 1433
opportunity :: 1434
serial :: 1435
intended :: 1436
forever :: 1437
emotions :: 1438
available :: 1439
victim :: 1440
charlie :: 1441
dies :: 1442
changes :: 1443
compelling :: 1444
bed :: 1445
six :: 1446
born :: 1447
happening :: 1448
bar :: 1449
paris :: 1450
likable :: 1451
lived :: 1452
twice :: 1453
falling :: 1454
hotel :: 1455
zero :: 1456
puts :: 1457
tired :: 1458
image :: 1459
pain :: 1460
lover :: 1461
everybody :: 1462
giant :: 1463
offer :: 1464
shock :: 1465
spot :: 1466
suggest :: 1467
j :: 1468
henry :: 1469
include :: 1470
confused :: 1471
trailer :: 1472
adults :: 1473
difference :: 1474
student :: 1475
fresh :: 1476
followed :: 1477
bruce :: 1478
r :: 1479
kelly :: 1480
hasn't :: 1481
appeared :: 1482
approach :: 1483
victims :: 1484
christian :: 1485
fellow :: 1486
hurt :: 1487
impact :: 1488
putting :: 1489
gorgeous :: 1490
step :: 1491
sub :: 1492
mix :: 1493
event :: 1494
notice :: 1495
murders :: 1496
share :: 1497
laughed :: 1498
confusing :: 1499
content :: 1500
mediocre :: 1501
11 :: 1502
lacks :: 1503
direct :: 1504
supposedly :: 1505
summer :: 1506
actresses :: 1507
flaws :: 1508
porn :: 1509
system :: 1510
page :: 1511
holes :: 1512
wall :: 1513
billy :: 1514
moral :: 1515
jerry :: 1516
worthy :: 1517
creative :: 1518
relationships :: 1519
rape :: 1520
tragedy :: 1521
race :: 1522
thin :: 1523
lighting :: 1524
helps :: 1525
random :: 1526
answer :: 1527
gem :: 1528
funniest :: 1529
ii :: 1530
americans :: 1531
jones :: 1532
merely :: 1533
proves :: 1534
wondering :: 1535
alien :: 1536
students :: 1537
ray :: 1538
paid :: 1539
al :: 1540
land :: 1541
seven :: 1542
damn :: 1543
agent :: 1544
delivers :: 1545
imagination :: 1546
park :: 1547
childhood :: 1548
flying :: 1549
hospital :: 1550
forgotten :: 1551
90 :: 1552
standards :: 1553
flicks :: 1554
impressed :: 1555
finding :: 1556
absolute :: 1557
ugly :: 1558
beat :: 1559
jean :: 1560
don :: 1561
thoroughly :: 1562
ms :: 1563
attractive :: 1564
ground :: 1565
negative :: 1566
wise :: 1567
provides :: 1568
latter :: 1569
50 :: 1570
stuck :: 1571
extreme :: 1572
seemingly :: 1573
seconds :: 1574
becoming :: 1575
winning :: 1576
addition :: 1577
reminded :: 1578
tragic :: 1579
offers :: 1580
inspired :: 1581
count :: 1582
fell :: 1583
thats :: 1584
lose :: 1585
affair :: 1586
turning :: 1587
folks :: 1588
detail :: 1589
faces :: 1590
cliché :: 1591
design :: 1592
martin :: 1593
collection :: 1594
afraid :: 1595
intense :: 1596
fashion :: 1597
pull :: 1598
hidden :: 1599
industry :: 1600
man's :: 1601
allen :: 1602
apartment :: 1603
o :: 1604
quick :: 1605
nasty :: 1606
arthur :: 1607
adds :: 1608
area :: 1609
rented :: 1610
alan :: 1611
angry :: 1612
personality :: 1613
artistic :: 1614
length :: 1615
shouldn't :: 1616
therefore :: 1617
information :: 1618
chinese :: 1619
brian :: 1620
shocking :: 1621
location :: 1622
ready :: 1623
professional :: 1624
lets :: 1625
animal :: 1626
anymore :: 1627
games :: 1628
teen :: 1629
states :: 1630
soldier :: 1631
listen :: 1632
mom :: 1633
describe :: 1634
lord :: 1635
news :: 1636
picked :: 1637
led :: 1638
wooden :: 1639
favourite :: 1640
dirty :: 1641
mouth :: 1642
asks :: 1643
food :: 1644
deliver :: 1645
onto :: 1646
martial :: 1647
bond :: 1648
clothes :: 1649
wars :: 1650
struggle :: 1651
queen :: 1652
redeeming :: 1653
stone :: 1654
jason :: 1655
scientist :: 1656
p :: 1657
wearing :: 1658
ed :: 1659
stephen :: 1660
compare :: 1661
castle :: 1662
intelligence :: 1663
creature :: 1664
cross :: 1665
sleep :: 1666
teenage :: 1667
allowed :: 1668
wonderfully :: 1669
necessary :: 1670
carry :: 1671
drugs :: 1672
40 :: 1673
tears :: 1674
fox :: 1675
criminal :: 1676
rip :: 1677
helped :: 1678
member :: 1679
desperate :: 1680
moved :: 1681
sight :: 1682
cgi :: 1683
trust :: 1684
deeply :: 1685
roll :: 1686
includes :: 1687
willing :: 1688
whatsoever :: 1689
disaster :: 1690
12 :: 1691
machine :: 1692
ship :: 1693
treat :: 1694
began :: 1695
mid :: 1696
uncle :: 1697
grace :: 1698
phone :: 1699
70's :: 1700
williams :: 1701
commentary :: 1702
build :: 1703
accident :: 1704
captain :: 1705
realized :: 1706
plane :: 1707
energy :: 1708
station :: 1709
warning :: 1710
epic :: 1711
davis :: 1712
rarely :: 1713
humans :: 1714
loving :: 1715
theatre :: 1716
comedic :: 1717
witch :: 1718
pop :: 1719
suicide :: 1720
dying :: 1721
powers :: 1722
filmmaker :: 1723
independent :: 1724
introduced :: 1725
nightmare :: 1726
extra :: 1727
engaging :: 1728
actions :: 1729
character's :: 1730
superior :: 1731
unusual :: 1732
arts :: 1733
apparent :: 1734
suit :: 1735
religious :: 1736
heroes :: 1737
danny :: 1738
remarkable :: 1739
artist :: 1740
allow :: 1741
pleasure :: 1742
continue :: 1743
unnecessary :: 1744
x :: 1745
ring :: 1746
returns :: 1747
physical :: 1748
sky :: 1749
teacher :: 1750
pre :: 1751
mental :: 1752
watchable :: 1753
provide :: 1754
absurd :: 1755
tim :: 1756
memory :: 1757
grand :: 1758
technical :: 1759
normally :: 1760
wedding :: 1761
desire :: 1762
limited :: 1763
anywhere :: 1764
scared :: 1765
russian :: 1766
surprising :: 1767
douglas :: 1768
finished :: 1769
brutal :: 1770
skip :: 1771
vision :: 1772
process :: 1773
intriguing :: 1774
bloody :: 1775
media :: 1776
holds :: 1777
exist :: 1778
accept :: 1779
nicely :: 1780
suspect :: 1781
000 :: 1782
jump :: 1783
twenty :: 1784
paced :: 1785
wanting :: 1786
search :: 1787
cops :: 1788
torture :: 1789
growing :: 1790
reminds :: 1791
jr :: 1792
according :: 1793
pacing :: 1794
legend :: 1795
soft :: 1796
passion :: 1797
andy :: 1798
player :: 1799
hated :: 1800
bits :: 1801
fred :: 1802
asked :: 1803
faith :: 1804
joy :: 1805
johnny :: 1806
clichés :: 1807
jeff :: 1808
academy :: 1809
dressed :: 1810
pilot :: 1811
eddie :: 1812
constant :: 1813
anybody :: 1814
ill :: 1815
deserved :: 1816
horse :: 1817
gold :: 1818
drunk :: 1819
joan :: 1820
blame :: 1821
originally :: 1822
explanation :: 1823
dangerous :: 1824
instance :: 1825
smile :: 1826
heaven :: 1827
heads :: 1828
sat :: 1829
community :: 1830
england :: 1831
superman :: 1832
deserve :: 1833
issue :: 1834
nonsense :: 1835
met :: 1836
dick :: 1837
lies :: 1838
capture :: 1839
gotten :: 1840
toward :: 1841
kevin :: 1842
somebody :: 1843
soap :: 1844
field :: 1845
lovers :: 1846
plots :: 1847
taylor :: 1848
mixed :: 1849
players :: 1850
nick :: 1851
explained :: 1852
record :: 1853
fail :: 1854
creating :: 1855
vhs :: 1856
knowledge :: 1857
quiet :: 1858
unknown :: 1859
fights :: 1860
starting :: 1861
friendship :: 1862
accurate :: 1863
whilst :: 1864
guns :: 1865
price :: 1866
adam :: 1867
kate :: 1868
hadn't :: 1869
sucks :: 1870
ball :: 1871
river :: 1872
floor :: 1873
european :: 1874
spanish :: 1875
wide :: 1876
cable :: 1877
radio :: 1878
fu :: 1879
cars :: 1880
jackson :: 1881
realism :: 1882
memories :: 1883
moon :: 1884
finest :: 1885
heroine :: 1886
aware :: 1887
loose :: 1888
eating :: 1889
featuring :: 1890
prince :: 1891
lacking :: 1892
responsible :: 1893
saved :: 1894
keeping :: 1895
empty :: 1896
understanding :: 1897
japan :: 1898
treated :: 1899
eat :: 1900
results :: 1901
cuts :: 1902
ice :: 1903
bland :: 1904
terribly :: 1905
pulled :: 1906
saving :: 1907
below :: 1908
officer :: 1909
villains :: 1910
candy :: 1911
broken :: 1912
sign :: 1913
ladies :: 1914
hopes :: 1915
rubbish :: 1916
delightful :: 1917
vs :: 1918
judge :: 1919
witty :: 1920
manage :: 1921
fat :: 1922
mine :: 1923
gene :: 1924
noticed :: 1925
included :: 1926
bright :: 1927
months :: 1928
forces :: 1929
screaming :: 1930
higher :: 1931
kinda :: 1932
wind :: 1933
tarzan :: 1934
cage :: 1935
hits :: 1936
loss :: 1937
today's :: 1938
monsters :: 1939
youth :: 1940
sing :: 1941
numerous :: 1942
partner :: 1943
conflict :: 1944
whenever :: 1945
humanity :: 1946
concerned :: 1947
pretentious :: 1948
fate :: 1949
singer :: 1950
dealing :: 1951
mike :: 1952
driving :: 1953
jesus :: 1954
private :: 1955
talents :: 1956
discovered :: 1957
naturally :: 1958
skills :: 1959
unfunny :: 1960
opposite :: 1961
finale :: 1962
bigger :: 1963
v :: 1964
ann :: 1965
international :: 1966
dated :: 1967
kick :: 1968
ups :: 1969
prove :: 1970
perspective :: 1971
morning :: 1972
mission :: 1973
discover :: 1974
portray :: 1975
blonde :: 1976
here's :: 1977
loses :: 1978
locations :: 1979
visit :: 1980
ordinary :: 1981
bank :: 1982
m :: 1983
humorous :: 1984
werewolf :: 1985
streets :: 1986
psychological :: 1987
regular :: 1988
reviewers :: 1989
received :: 1990
kong :: 1991
w :: 1992
edited :: 1993
gags :: 1994
ass :: 1995
luck :: 1996
curious :: 1997
gary :: 1998
continues :: 1999
magnificent :: 2000
13 :: 2001
we've :: 2002
behavior :: 2003
captured :: 2004
jimmy :: 2005
satire :: 2006
survive :: 2007
context :: 2008
visually :: 2009
breaks :: 2010
existence :: 2011
shallow :: 2012
opens :: 2013
l :: 2014
mrs :: 2015
debut :: 2016
advice :: 2017
calls :: 2018
sea :: 2019
foot :: 2020
morgan :: 2021
shop :: 2022
h :: 2023
murdered :: 2024
connection :: 2025
core :: 2026
essentially :: 2027
current :: 2028
revealed :: 2029
director's :: 2030
corny :: 2031
remembered :: 2032
deals :: 2033
blind :: 2034
frankly :: 2035
occasionally :: 2036
lesson :: 2037
genuine :: 2038
scream :: 2039
traditional :: 2040
they've :: 2041
lucky :: 2042
identity :: 2043
dimensional :: 2044
african :: 2045
bob :: 2046
anthony :: 2047
efforts :: 2048
sean :: 2049
golden :: 2050
learned :: 2051
segment :: 2052
stock :: 2053
window :: 2054
cameo :: 2055
owner :: 2056
visuals :: 2057
versions :: 2058
village :: 2059
albert :: 2060
develop :: 2061
santa :: 2062
formula :: 2063
miles :: 2064
keaton :: 2065
one's :: 2066
sucked :: 2067
decade :: 2068
buddy :: 2069
genuinely :: 2070
grown :: 2071
references :: 2072
suffering :: 2073
boat :: 2074
lewis :: 2075
unexpected :: 2076
favor :: 2077
study :: 2078
washington :: 2079
allows :: 2080
program :: 2081
national :: 2082
grew :: 2083
80s :: 2084
proved :: 2085
meanwhile :: 2086
overly :: 2087
ages :: 2088
board :: 2089
standing :: 2090
logic :: 2091
desert :: 2092
spectacular :: 2093
awkward :: 2094
ultimate :: 2095
comparison :: 2096
reaction :: 2097
rob :: 2098
sheer :: 2099
jennifer :: 2100
reach :: 2101
thomas :: 2102
unable :: 2103
failure :: 2104
brilliantly :: 2105
travel :: 2106
grant :: 2107
ford :: 2108
vampires :: 2109
types :: 2110
parody :: 2111
gangster :: 2112
devil :: 2113
steal :: 2114
brown :: 2115
passed :: 2116
sudden :: 2117
stereotypes :: 2118
sake :: 2119
flesh :: 2120
leader :: 2121
frame :: 2122
bear :: 2123
strength :: 2124
speed :: 2125
creates :: 2126
eric :: 2127
awards :: 2128
laughter :: 2129
dan :: 2130
technology :: 2131
delivered :: 2132
author :: 2133
bet :: 2134
kung :: 2135
crappy :: 2136
wood :: 2137
site :: 2138
broadway :: 2139
insane :: 2140
trek :: 2141
executed :: 2142
relief :: 2143
lake :: 2144
hitler :: 2145
gonna :: 2146
discovers :: 2147
emotionally :: 2148
painfully :: 2149
dreadful :: 2150
marie :: 2151
utter :: 2152
commercial :: 2153
decision :: 2154
code :: 2155
steven :: 2156
fault :: 2157
anime :: 2158
majority :: 2159
anne :: 2160
round :: 2161
pair :: 2162
robin :: 2163
caused :: 2164
bomb :: 2165
families :: 2166
psycho :: 2167
driven :: 2168
attitude :: 2169
clean :: 2170
built :: 2171
gratuitous :: 2172
harris :: 2173
native :: 2174
luke :: 2175
entertained :: 2176
graphic :: 2177
ran :: 2178
killers :: 2179
meeting :: 2180
test :: 2181
simon :: 2182
flashbacks :: 2183
underrated :: 2184
nevertheless :: 2185
model :: 2186
seasons :: 2187
asian :: 2188
foreign :: 2189
hill :: 2190
levels :: 2191
obsessed :: 2192
evening :: 2193
feet :: 2194
halloween :: 2195
vehicle :: 2196
barbara :: 2197
relate :: 2198
treatment :: 2199
rise :: 2200
practically :: 2201
range :: 2202
endless :: 2203
freedom :: 2204
costs :: 2205
religion :: 2206
gory :: 2207
cash :: 2208
described :: 2209
wit :: 2210
pleasant :: 2211
aged :: 2212
ancient :: 2213
tape :: 2214
reviewer :: 2215
center :: 2216
president :: 2217
chosen :: 2218
lynch :: 2219
product :: 2220
combination :: 2221
send :: 2222
fly :: 2223
seat :: 2224
sell :: 2225
70s :: 2226
irritating :: 2227
exploitation :: 2228
excited :: 2229
stopped :: 2230
hearing :: 2231
rescue :: 2232
fill :: 2233
howard :: 2234
portrays :: 2235
gordon :: 2236
assume :: 2237
parker :: 2238
classics :: 2239
pity :: 2240
0 :: 2241
produce :: 2242
hunter :: 2243
breaking :: 2244
dry :: 2245
fame :: 2246
anna :: 2247
generation :: 2248
sheriff :: 2249
capable :: 2250
believes :: 2251
handsome :: 2252
theatrical :: 2253
asking :: 2254
sports :: 2255
largely :: 2256
choose :: 2257
theaters :: 2258
sympathetic :: 2259
extras :: 2260
proper :: 2261
ruined :: 2262
cares :: 2263
contrived :: 2264
portraying :: 2265
drew :: 2266
individual :: 2267
embarrassing :: 2268
rules :: 2269
unrealistic :: 2270
learns :: 2271
warm :: 2272
victor :: 2273
daniel :: 2274
marry :: 2275
appealing :: 2276
safe :: 2277
dubbed :: 2278
depressing :: 2279
canadian :: 2280
freddy :: 2281
shakespeare :: 2282
recall :: 2283
chick :: 2284
uk :: 2285
winner :: 2286
hearted :: 2287
contrast :: 2288
sequels :: 2289
involves :: 2290
par :: 2291
woody :: 2292
crowd :: 2293
matters :: 2294
k :: 2295
correct :: 2296
chief :: 2297
costume :: 2298
haunting :: 2299
paper :: 2300
research :: 2301
vote :: 2302
strongly :: 2303
heck :: 2304
nominated :: 2305
grow :: 2306
clue :: 2307
claim :: 2308
facts :: 2309
eight :: 2310
protagonist :: 2311
matt :: 2312
rose :: 2313
evidence :: 2314
joseph :: 2315
appropriate :: 2316
disgusting :: 2317
excitement :: 2318
football :: 2319
lousy :: 2320
germany :: 2321
cost :: 2322
france :: 2323
saturday :: 2324
priest :: 2325
talks :: 2326
substance :: 2327
losing :: 2328
patrick :: 2329
destroy :: 2330
circumstances :: 2331
tedious :: 2332
training :: 2333
thoughts :: 2334
hunt :: 2335
market :: 2336
scare :: 2337
voices :: 2338
promise :: 2339
naive :: 2340
bringing :: 2341
amateurish :: 2342
teenager :: 2343
angel :: 2344
walter :: 2345
captures :: 2346
convinced :: 2347
hanging :: 2348
satisfying :: 2349
bodies :: 2350
united :: 2351
fits :: 2352
tend :: 2353
jackie :: 2354
trilogy :: 2355
roy :: 2356
horribly :: 2357
lower :: 2358
asleep :: 2359
virtually :: 2360
baseball :: 2361
robot :: 2362
hopefully :: 2363
rental :: 2364
alex :: 2365
com :: 2366
factor :: 2367
haunted :: 2368
teenagers :: 2369
hall :: 2370
walks :: 2371
spoil :: 2372
creatures :: 2373
amateur :: 2374
relatively :: 2375
steals :: 2376
mask :: 2377
welcome :: 2378
cinderella :: 2379
covered :: 2380
ryan :: 2381
danger :: 2382
europe :: 2383
insult :: 2384
category :: 2385
continuity :: 2386
mini :: 2387
unlikely :: 2388
drag :: 2389
sinatra :: 2390
skin :: 2391
contemporary :: 2392
louis :: 2393
semi :: 2394
viewed :: 2395
fare :: 2396
north :: 2397
influence :: 2398
depicted :: 2399
handled :: 2400
target :: 2401
oliver :: 2402
offensive :: 2403
hat :: 2404
initial :: 2405
nancy :: 2406
scale :: 2407
lawyer :: 2408
tiny :: 2409
cutting :: 2410
unfortunate :: 2411
holding :: 2412
witness :: 2413
shocked :: 2414
africa :: 2415
remain :: 2416
believed :: 2417
fool :: 2418
inner :: 2419
politics :: 2420
hide :: 2421
reporter :: 2422
presents :: 2423
section :: 2424
movement :: 2425
provided :: 2426
surreal :: 2427
promising :: 2428
designed :: 2429
makeup :: 2430
max :: 2431
qualities :: 2432
liners :: 2433
refreshing :: 2434
australian :: 2435
source :: 2436
14 :: 2437
structure :: 2438
closer :: 2439
drop :: 2440
forgettable :: 2441
touches :: 2442
welles :: 2443
display :: 2444
angles :: 2445
pile :: 2446
fairy :: 2447
repeated :: 2448
till :: 2449
texas :: 2450
wayne :: 2451
claims :: 2452
previously :: 2453
faced :: 2454
sharp :: 2455
deaths :: 2456
ruin :: 2457
accents :: 2458
surprises :: 2459
universal :: 2460
degree :: 2461
focused :: 2462
propaganda :: 2463
plans :: 2464
serves :: 2465
speaks :: 2466
supernatural :: 2467
highlight :: 2468
service :: 2469
peace :: 2470
chose :: 2471
related :: 2472
cartoons :: 2473
adventures :: 2474
erotic :: 2475
25 :: 2476
roger :: 2477
suffers :: 2478
blow :: 2479
weekend :: 2480
sisters :: 2481
granted :: 2482
mainstream :: 2483
latest :: 2484
weeks :: 2485
prime :: 2486
crash :: 2487
cant :: 2488
professor :: 2489
experiences :: 2490
speech :: 2491
print :: 2492
lesbian :: 2493
harsh :: 2494
deadly :: 2495
veteran :: 2496
mistakes :: 2497
edward :: 2498
routine :: 2499
whoever :: 2500
notch :: 2501
uninteresting :: 2502
realizes :: 2503
invisible :: 2504
combined :: 2505
sympathy :: 2506
accidentally :: 2507
kim :: 2508
twisted :: 2509
brave :: 2510
colors :: 2511
dollars :: 2512
security :: 2513
draw :: 2514
dogs :: 2515
nude :: 2516
rain :: 2517
universe :: 2518
struggling :: 2519
dozen :: 2520
teens :: 2521
convince :: 2522
guilty :: 2523
path :: 2524
appreciated :: 2525
atrocious :: 2526
mountain :: 2527
treasure :: 2528
walked :: 2529
columbo :: 2530
irish :: 2531
frightening :: 2532
would've :: 2533
committed :: 2534
aliens :: 2535
technically :: 2536
recognize :: 2537
cowboy :: 2538
blah :: 2539
birth :: 2540
enter :: 2541
gritty :: 2542
enemy :: 2543
aka :: 2544
spy :: 2545
changing :: 2546
magical :: 2547
anderson :: 2548
princess :: 2549
department :: 2550
gas :: 2551
occasional :: 2552
friday :: 2553
sword :: 2554
directly :: 2555
false :: 2556
massive :: 2557
surface :: 2558
narration :: 2559
legendary :: 2560
featured :: 2561
victoria :: 2562
anger :: 2563
offered :: 2564
paint :: 2565
performed :: 2566
moore :: 2567
explains :: 2568
abuse :: 2569
suspenseful :: 2570
vietnam :: 2571
kinds :: 2572
terror :: 2573
experienced :: 2574
friendly :: 2575
subtitles :: 2576
reputation :: 2577
crying :: 2578
hong :: 2579
sorts :: 2580
passing :: 2581
junk :: 2582
beach :: 2583
multiple :: 2584
forest :: 2585
stolen :: 2586
everywhere :: 2587
figures :: 2588
forth :: 2589
statement :: 2590
exact :: 2591
powell :: 2592
variety :: 2593
required :: 2594
clark :: 2595
reveal :: 2596
donald :: 2597
regret :: 2598
conversation :: 2599
prior :: 2600
darkness :: 2601
remotely :: 2602
execution :: 2603
theory :: 2604
trapped :: 2605
proud :: 2606
belief :: 2607
urban :: 2608
russell :: 2609
lonely :: 2610
placed :: 2611
downright :: 2612
wilson :: 2613
san :: 2614
fictional :: 2615
melodrama :: 2616
spends :: 2617
insight :: 2618
court :: 2619
effectively :: 2620
listening :: 2621
grave :: 2622
express :: 2623
demons :: 2624
crude :: 2625
figured :: 2626
bothered :: 2627
abandoned :: 2628
scares :: 2629
network :: 2630
unconvincing :: 2631
jobs :: 2632
hired :: 2633
revolution :: 2634
favorites :: 2635
jon :: 2636
wear :: 2637
minds :: 2638
metal :: 2639
worthwhile :: 2640
emma :: 2641
california :: 2642
dean :: 2643
buying :: 2644
blockbuster :: 2645
lifetime :: 2646
bus :: 2647
paying :: 2648
pulls :: 2649
account :: 2650
angle :: 2651
happiness :: 2652
von :: 2653
blown :: 2654
afternoon :: 2655
imagery :: 2656
rights :: 2657
driver :: 2658
alright :: 2659
rolling :: 2660
matrix :: 2661
mexican :: 2662
productions :: 2663
amazed :: 2664
idiot :: 2665
rings :: 2666
cultural :: 2667
status :: 2668
delivery :: 2669
thankfully :: 2670
grim :: 2671
reveals :: 2672
rule :: 2673
stayed :: 2674
handed :: 2675
alice :: 2676
stays :: 2677
scenario :: 2678
focuses :: 2679
ha :: 2680
significant :: 2681
quest :: 2682
rough :: 2683
starred :: 2684
examples :: 2685
julia :: 2686
jungle :: 2687
sir :: 2688
indie :: 2689
lights :: 2690
mere :: 2691
views :: 2692
murphy :: 2693
shadow :: 2694
sarah :: 2695
bore :: 2696
con :: 2697
teeth :: 2698
heavily :: 2699
mature :: 2700
device :: 2701
table :: 2702
skill :: 2703
interview :: 2704
caine :: 2705
tight :: 2706
necessarily :: 2707
he'd :: 2708
ron :: 2709
sunday :: 2710
clichéd :: 2711
suffer :: 2712
mexico :: 2713
china :: 2714
achieve :: 2715
spite :: 2716
understood :: 2717
format :: 2718
artists :: 2719
position :: 2720
initially :: 2721
closing :: 2722
campy :: 2723
desperately :: 2724
bound :: 2725
fabulous :: 2726
dress :: 2727
sensitive :: 2728
mgm :: 2729
destroyed :: 2730
hip :: 2731
complicated :: 2732
burns :: 2733
demon :: 2734
summary :: 2735
seek :: 2736
faithful :: 2737
forgot :: 2738
sun :: 2739
decades :: 2740
breath :: 2741
gross :: 2742
pitt :: 2743
bourne :: 2744
ghosts :: 2745
titanic :: 2746
cruel :: 2747
murderer :: 2748
stereotypical :: 2749
deeper :: 2750
lisa :: 2751
facial :: 2752
renting :: 2753
ignore :: 2754
pregnant :: 2755
league :: 2756
answers :: 2757
racist :: 2758
un :: 2759
helping :: 2760
ludicrous :: 2761
beloved :: 2762
flashback :: 2763
slapstick :: 2764
sleeping :: 2765
17 :: 2766
dude :: 2767
cell :: 2768
musicals :: 2769
fourth :: 2770
wing :: 2771
intellectual :: 2772
beast :: 2773
sounded :: 2774
settings :: 2775
environment :: 2776
suck :: 2777
critical :: 2778
drinking :: 2779
nazi :: 2780
reminiscent :: 2781
brad :: 2782
calling :: 2783
lugosi :: 2784
dragon :: 2785
description :: 2786
susan :: 2787
prefer :: 2788
amazingly :: 2789
task :: 2790
mildly :: 2791
pacino :: 2792
disbelief :: 2793
encounter :: 2794
regarding :: 2795
larry :: 2796
inept :: 2797
greater :: 2798
learning :: 2799
arms :: 2800
dennis :: 2801
extraordinary :: 2802
turkey :: 2803
storytelling :: 2804
funnier :: 2805
julie :: 2806
halfway :: 2807
ain't :: 2808
expert :: 2809
base :: 2810
criticism :: 2811
quirky :: 2812
father's :: 2813
leslie :: 2814
warned :: 2815
cabin :: 2816
flight :: 2817
titles :: 2818
criminals :: 2819
johnson :: 2820
raw :: 2821
praise :: 2822
depiction :: 2823
screening :: 2824
throwing :: 2825
extent :: 2826
expression :: 2827
kiss :: 2828
jail :: 2829
studios :: 2830
freeman :: 2831
truck :: 2832
convey :: 2833
originality :: 2834
chan :: 2835
entertain :: 2836
choices :: 2837
spoof :: 2838
notorious :: 2839
tree :: 2840
raised :: 2841
touched :: 2842
children's :: 2843
rachel :: 2844
punch :: 2845
experiment :: 2846
daughters :: 2847
prepared :: 2848
comical :: 2849
spoken :: 2850
people's :: 2851
timing :: 2852
india :: 2853
headed :: 2854
purely :: 2855
could've :: 2856
basis :: 2857
hoffman :: 2858
bollywood :: 2859
chilling :: 2860
michelle :: 2861
underground :: 2862
dollar :: 2863
via :: 2864
picks :: 2865
lie :: 2866
inspiration :: 2867
novels :: 2868
wave :: 2869
elizabeth :: 2870
introduction :: 2871
weapons :: 2872
trick :: 2873
lazy :: 2874
jessica :: 2875
graphics :: 2876
breathtaking :: 2877
notable :: 2878
stomach :: 2879
succeeds :: 2880
term :: 2881
crafted :: 2882
join :: 2883
throws :: 2884
handle :: 2885
strangely :: 2886
properly :: 2887
toy :: 2888
nowadays :: 2889
christ :: 2890
sidney :: 2891
reference :: 2892
adding :: 2893
claire :: 2894
serve :: 2895
ratings :: 2896
locked :: 2897
honor :: 2898
wears :: 2899
sitcom :: 2900
ted :: 2901
authentic :: 2902
foster :: 2903
regard :: 2904
everyday :: 2905
causes :: 2906
maria :: 2907
provoking :: 2908
charge :: 2909
protect :: 2910
lesser :: 2911
hitchcock :: 2912
caring :: 2913
mouse :: 2914
mirror :: 2915
bat :: 2916
fallen :: 2917
carrying :: 2918
bitter :: 2919
jewish :: 2920
established :: 2921
pet :: 2922
amongst :: 2923
east :: 2924
shut :: 2925
guard :: 2926
midnight :: 2927
sleazy :: 2928
southern :: 2929
determined :: 2930
ned :: 2931
challenge :: 2932
daily :: 2933
obnoxious :: 2934
nonetheless :: 2935
cases :: 2936
carried :: 2937
carries :: 2938
wins :: 2939
alas :: 2940
remote :: 2941
embarrassed :: 2942
gruesome :: 2943
hole :: 2944
2006 :: 2945
lane :: 2946
attempting :: 2947
westerns :: 2948
escapes :: 2949
sinister :: 2950
confusion :: 2951
nation :: 2952
tales :: 2953
ironic :: 2954
tradition :: 2955
interpretation :: 2956
arrives :: 2957
busy :: 2958
replaced :: 2959
risk :: 2960
enjoying :: 2961
sold :: 2962
essential :: 2963
needless :: 2964
aunt :: 2965
hardy :: 2966
burt :: 2967
goofy :: 2968
mass :: 2969
obsession :: 2970
minded :: 2971
balance :: 2972
flow :: 2973
clips :: 2974
existent :: 2975
successfully :: 2976
legs :: 2977
presentation :: 2978
screenwriter :: 2979
jumps :: 2980
exists :: 2981
attacked :: 2982
blair :: 2983
laid :: 2984
mentally :: 2985
bbc :: 2986
seeking :: 2987
raise :: 2988
topic :: 2989
oddly :: 2990
warner :: 2991
inspector :: 2992
horrific :: 2993
fortunately :: 2994
shape :: 2995
marvelous :: 2996
usa :: 2997
intentions :: 2998
buck :: 2999
retarded :: 3000
madness :: 3001
stupidity :: 3002
stops :: 3003
text :: 3004
stylish :: 3005
stanley :: 3006
che :: 3007
rival :: 3008
served :: 3009
workers :: 3010
maker :: 3011
sides :: 3012
ashamed :: 3013
shower :: 3014
packed :: 3015
comedian :: 3016
thrilling :: 3017
wwii :: 3018
interviews :: 3019
nine :: 3020
laura :: 3021
frequently :: 3022
upper :: 3023
mob :: 3024
mansion :: 3025
bridge :: 3026
remind :: 3027
tongue :: 3028
navy :: 3029
wanna :: 3030
contain :: 3031
albeit :: 3032
intensity :: 3033
attacks :: 3034
vacation :: 3035
thief :: 3036
delight :: 3037
manager :: 3038
chair :: 3039
sum :: 3040
hence :: 3041
80 :: 3042
cheese :: 3043
drives :: 3044
2001 :: 3045
expressions :: 3046
struggles :: 3047
flawed :: 3048
poignant :: 3049
angels :: 3050
personalities :: 3051
rogers :: 3052
riding :: 3053
revolves :: 3054
refuses :: 3055
adapted :: 3056
opened :: 3057
greatly :: 3058
credibility :: 3059
philip :: 3060
cooper :: 3061
glass :: 3062
pitch :: 3063
tracy :: 3064
1950s :: 3065
jay :: 3066
torn :: 3067
dinner :: 3068
bette :: 3069
18 :: 3070
cynical :: 3071
upset :: 3072
pool :: 3073
sin :: 3074
tour :: 3075
2000 :: 3076
internet :: 3077
suspects :: 3078
advantage :: 3079
lessons :: 3080
warn :: 3081
lion :: 3082
overcome :: 3083
credible :: 3084
wishes :: 3085
thousands :: 3086
spin :: 3087
miller :: 3088
racism :: 3089
90's :: 3090
mindless :: 3091
wealthy :: 3092
innocence :: 3093
tense :: 3094
broke :: 3095
bugs :: 3096
happily :: 3097
catholic :: 3098
guessing :: 3099
trial :: 3100
lucy :: 3101
hood :: 3102
hundreds :: 3103
trite :: 3104
physically :: 3105
thrillers :: 3106
cook :: 3107
fish :: 3108
alike :: 3109
dubbing :: 3110
fbi :: 3111
crisis :: 3112
per :: 3113
pride :: 3114
succeed :: 3115
controversial :: 3116
suffered :: 3117
reed :: 3118
bag :: 3119
technique :: 3120
wasting :: 3121
dislike :: 3122
medical :: 3123
sexuality :: 3124
countries :: 3125
perform :: 3126
patient :: 3127
stranger :: 3128
enjoyment :: 3129
corner :: 3130
arm :: 3131
glimpse :: 3132
gripping :: 3133
reunion :: 3134
franchise :: 3135
holmes :: 3136
ensemble :: 3137
separate :: 3138
hundred :: 3139
lincoln :: 3140
60's :: 3141
sings :: 3142
noble :: 3143
shines :: 3144
whereas :: 3145
tied :: 3146
ourselves :: 3147
uncomfortable :: 3148
infamous :: 3149
neat :: 3150
atmospheric :: 3151
millions :: 3152
shorts :: 3153
contact :: 3154
card :: 3155
hint :: 3156
pack :: 3157
courage :: 3158
irony :: 3159
exceptional :: 3160
plastic :: 3161
storm :: 3162
drink :: 3163
ralph :: 3164
searching :: 3165
oscars :: 3166
scripts :: 3167
connected :: 3168
italy :: 3169
proof :: 3170
sandler :: 3171
snow :: 3172
lying :: 3173
flash :: 3174
nose :: 3175
curse :: 3176
helen :: 3177
sentimental :: 3178
mst3k :: 3179
grey :: 3180
aired :: 3181
holiday :: 3182
steps :: 3183
hills :: 3184
performers :: 3185
letting :: 3186
chasing :: 3187
suggests :: 3188
dancer :: 3189
tune :: 3190
meaningful :: 3191
idiotic :: 3192
knife :: 3193
quote :: 3194
weapon :: 3195
plague :: 3196
sons :: 3197
entry :: 3198
kurt :: 3199
fortune :: 3200
cameos :: 3201
consists :: 3202
perfection :: 3203
lovable :: 3204
hoped :: 3205
troubled :: 3206
thousand :: 3207
hiding :: 3208
develops :: 3209
unforgettable :: 3210
accepted :: 3211
noted :: 3212
portrait :: 3213
dear :: 3214
equal :: 3215
bettie :: 3216
assistant :: 3217
stretch :: 3218
woman's :: 3219
saves :: 3220
colorful :: 3221
annoyed :: 3222
larger :: 3223
attraction :: 3224
condition :: 3225
miscast :: 3226
chases :: 3227
brooks :: 3228
virgin :: 3229
spots :: 3230
basement :: 3231
host :: 3232
dialogs :: 3233
shoots :: 3234
gain :: 3235
horses :: 3236
guilt :: 3237
protagonists :: 3238
oil :: 3239
terrifying :: 3240
month :: 3241
cousin :: 3242
neighborhood :: 3243
vincent :: 3244
pg :: 3245
belongs :: 3246
stealing :: 3247
16 :: 3248
nelson :: 3249
worry :: 3250
burning :: 3251
concert :: 3252
ad :: 3253
zone :: 3254
strip :: 3255
appearing :: 3256
worlds :: 3257
object :: 3258
split :: 3259
repeat :: 3260
hang :: 3261
boredom :: 3262
destruction :: 3263
thirty :: 3264
redemption :: 3265
hunting :: 3266
encounters :: 3267
imaginative :: 3268
expensive :: 3269
eerie :: 3270
cube :: 3271
seagal :: 3272
jake :: 3273
pie :: 3274
competent :: 3275
homeless :: 3276
concerns :: 3277
andrew :: 3278
flaw :: 3279
closely :: 3280
bo :: 3281
ultra :: 3282
factory :: 3283
1st :: 3284
multi :: 3285
civil :: 3286
dramas :: 3287
gag :: 3288
stunts :: 3289
wake :: 3290
guts :: 3291
sends :: 3292
60 :: 3293
sutherland :: 3294
glory :: 3295
knock :: 3296
matthau :: 3297
massacre :: 3298
letter :: 3299
elsewhere :: 3300
achieved :: 3301
dig :: 3302
checking :: 3303
widmark :: 3304
hooked :: 3305
complaint :: 3306
neck :: 3307
endearing :: 3308
segments :: 3309
shark :: 3310
sullivan :: 3311
rushed :: 3312
virus :: 3313
ripped :: 3314
charisma :: 3315
incoherent :: 3316
dragged :: 3317
beating :: 3318
dentist :: 3319
essence :: 3320
bears :: 3321
profound :: 3322
library :: 3323
weight :: 3324
tear :: 3325
crimes :: 3326
arnold :: 3327
dare :: 3328
appearances :: 3329
solve :: 3330
trade :: 3331
pat :: 3332
24 :: 3333
stanwyck :: 3334
colour :: 3335
teach :: 3336
dorothy :: 3337
roberts :: 3338
rocks :: 3339
fest :: 3340
spell :: 3341
catherine :: 3342
dealt :: 3343
stan :: 3344
fitting :: 3345
hitting :: 3346
striking :: 3347
pro :: 3348
2005 :: 3349
tribute :: 3350
tricks :: 3351
60s :: 3352
battles :: 3353
believing :: 3354
briefly :: 3355
countless :: 3356
fashioned :: 3357
loser :: 3358
goal :: 3359
gothic :: 3360
noise :: 3361
techniques :: 3362
n :: 3363
videos :: 3364
health :: 3365
thumbs :: 3366
attempted :: 3367
scientists :: 3368
st :: 3369
painting :: 3370
baker :: 3371
strikes :: 3372
inspiring :: 3373
huh :: 3374
sexually :: 3375
birthday :: 3376
secretary :: 3377
curtis :: 3378
jeremy :: 3379
covers :: 3380
pointed :: 3381
slight :: 3382
specific :: 3383
tea :: 3384
hearts :: 3385
unintentionally :: 3386
denzel :: 3387
horrendous :: 3388
charismatic :: 3389
silver :: 3390
surrounded :: 3391
surrounding :: 3392
reactions :: 3393
branagh :: 3394
importance :: 3395
rochester :: 3396
admittedly :: 3397
carefully :: 3398
jerk :: 3399
tons :: 3400
hype :: 3401
relevant :: 3402
they'd :: 3403
walls :: 3404
stood :: 3405
eyed :: 3406
bible :: 3407
corrupt :: 3408
rush :: 3409
stunt :: 3410
revelation :: 3411
smoking :: 3412
magazine :: 3413
lloyd :: 3414
kicks :: 3415
karloff :: 3416
stronger :: 3417
grows :: 3418
mild :: 3419
hamlet :: 3420
represents :: 3421
dawn :: 3422
andrews :: 3423
intention :: 3424
easier :: 3425
enters :: 3426
spending :: 3427
scooby :: 3428
fired :: 3429
killings :: 3430
stated :: 3431
chances :: 3432
shall :: 3433
brand :: 3434
exercise :: 3435
university :: 3436
increasingly :: 3437
row :: 3438
disagree :: 3439
cardboard :: 3440
winter :: 3441
comics :: 3442
requires :: 3443
dropped :: 3444
associated :: 3445
world's :: 3446
chuck :: 3447
iii :: 3448
medium :: 3449
bush :: 3450
projects :: 3451
bride :: 3452
occurs :: 3453
korean :: 3454
inevitable :: 3455
messages :: 3456
brando :: 3457
le :: 3458
strike :: 3459
poverty :: 3460
forgive :: 3461
performing :: 3462
stiff :: 3463
attached :: 3464
drags :: 3465
luckily :: 3466
ian :: 3467
identify :: 3468
1970s :: 3469
gift :: 3470
bobby :: 3471
acceptable :: 3472
resolution :: 3473
eva :: 3474
typically :: 3475
canada :: 3476
guest :: 3477
nuclear :: 3478
elvis :: 3479
toilet :: 3480
strictly :: 3481
vague :: 3482
spike :: 3483
contract :: 3484
hire :: 3485
1980s :: 3486
thrills :: 3487
selling :: 3488
hudson :: 3489
homage :: 3490
lab :: 3491
boll :: 3492
mafia :: 3493
depression :: 3494
sophisticated :: 3495
fifteen :: 3496
disease :: 3497
allowing :: 3498
brilliance :: 3499
investigation :: 3500
continued :: 3501
struck :: 3502
insulting :: 3503
worker :: 3504
instantly :: 3505
useless :: 3506
breasts :: 3507
barry :: 3508
jesse :: 3509
sally :: 3510
afterwards :: 3511
chaplin :: 3512
britain :: 3513
carter :: 3514
executive :: 3515
handful :: 3516
importantly :: 3517
godfather :: 3518
estate :: 3519
hanks :: 3520
pleased :: 3521
overlooked :: 3522
evident :: 3523
burn :: 3524
gotta :: 3525
wreck :: 3526
nights :: 3527
2002 :: 3528
beings :: 3529
ego :: 3530
kidnapped :: 3531
presumably :: 3532
competition :: 3533
press :: 3534
partly :: 3535
digital :: 3536
shining :: 3537
commit :: 3538
tremendous :: 3539
raped :: 3540
menacing :: 3541
silence :: 3542
talked :: 3543
derek :: 3544
worthless :: 3545
jamie :: 3546
realise :: 3547
ambitious :: 3548
meat :: 3549
wondered :: 3550
photographed :: 3551
sacrifice :: 3552
arrested :: 3553
buried :: 3554
burton :: 3555
threatening :: 3556
smooth :: 3557
aforementioned :: 3558
superbly :: 3559
boxing :: 3560
kane :: 3561
flawless :: 3562
regardless :: 3563
fears :: 3564
creation :: 3565
shy :: 3566
heat :: 3567
highlights :: 3568
savage :: 3569
persona :: 3570
frustrated :: 3571
drivel :: 3572
conspiracy :: 3573
individuals :: 3574
wonders :: 3575
listed :: 3576
appalling :: 3577
doc :: 3578
's :: 3579
spiritual :: 3580
pushed :: 3581
returning :: 3582
jumping :: 3583
elvira :: 3584
cox :: 3585
corpse :: 3586
size :: 3587
characterization :: 3588
bullets :: 3589
walken :: 3590
generous :: 3591
string :: 3592
rex :: 3593
doors :: 3594
pleasantly :: 3595
bucks :: 3596
relative :: 3597
45 :: 3598
outrageous :: 3599
kudos :: 3600
planning :: 3601
ticket :: 3602
achievement :: 3603
accomplished :: 3604
miserably :: 3605
monkey :: 3606
beaten :: 3607
neighbor :: 3608
distant :: 3609
fatal :: 3610
repetitive :: 3611
accused :: 3612
picking :: 3613
ironically :: 3614
consequences :: 3615
curiosity :: 3616
union :: 3617
admire :: 3618
guide :: 3619
splendid :: 3620
prevent :: 3621
reynolds :: 3622
border :: 3623
attracted :: 3624
butt :: 3625
clues :: 3626
trap :: 3627
notes :: 3628
chain :: 3629
opposed :: 3630
watches :: 3631
samurai :: 3632
shortly :: 3633
heston :: 3634
twin :: 3635
cole :: 3636
glover :: 3637
slightest :: 3638
response :: 3639
beer :: 3640
territory :: 3641
spooky :: 3642
diamond :: 3643
rap :: 3644
horrors :: 3645
20th :: 3646
cup :: 3647
dire :: 3648
spirited :: 3649
melodramatic :: 3650
lucas :: 3651
flynn :: 3652
los :: 3653
piano :: 3654
push :: 3655
revealing :: 3656
spoiled :: 3657
uninspired :: 3658
ritter :: 3659
convoluted :: 3660
pulling :: 3661
ken :: 3662
root :: 3663
they'll :: 3664
streisand :: 3665
motivation :: 3666
directorial :: 3667
installment :: 3668
precious :: 3669
titled :: 3670
logical :: 3671
documentaries :: 3672
spring :: 3673
lacked :: 3674
suits :: 3675
tall :: 3676
subplot :: 3677
mate :: 3678
timeless :: 3679
hatred :: 3680
throat :: 3681
blows :: 3682
jealous :: 3683
creators :: 3684
blank :: 3685
farce :: 3686
spielberg :: 3687
slap :: 3688
ward :: 3689
carol :: 3690
subsequent :: 3691
cared :: 3692
mile :: 3693
exaggerated :: 3694
duke :: 3695
morality :: 3696
liberal :: 3697
francisco :: 3698
indians :: 3699
psychotic :: 3700
overdone :: 3701
psychiatrist :: 3702
astaire :: 3703
intrigued :: 3704
jet :: 3705
blob :: 3706
50's :: 3707
conceived :: 3708
fx :: 3709
neil :: 3710
aimed :: 3711
remaining :: 3712
doo :: 3713
ignored :: 3714
elderly :: 3715
reasonably :: 3716
mitchell :: 3717
failing :: 3718
sole :: 3719
obscure :: 3720
drunken :: 3721
minimal :: 3722
temple :: 3723
progress :: 3724
fancy :: 3725
captivating :: 3726
repeatedly :: 3727
wes :: 3728
tunes :: 3729
shoes :: 3730
grandmother :: 3731
cia :: 3732
nurse :: 3733
marks :: 3734
notably :: 3735
emily :: 3736
soviet :: 3737
shirt :: 3738
explore :: 3739
smoke :: 3740
souls :: 3741
pushing :: 3742
argument :: 3743
distance :: 3744
warrior :: 3745
outcome :: 3746
reduced :: 3747
loosely :: 3748
scientific :: 3749
goldberg :: 3750
gradually :: 3751
bleak :: 3752
timothy :: 3753
manhattan :: 3754
idiots :: 3755
restaurant :: 3756
scripted :: 3757
misses :: 3758
explicit :: 3759
providing :: 3760
elaborate :: 3761
poster :: 3762
lou :: 3763
dignity :: 3764
carpenter :: 3765
norman :: 3766
rid :: 3767
turner :: 3768
show's :: 3769
davies :: 3770
draws :: 3771
discussion :: 3772
exposed :: 3773
mel :: 3774
sticks :: 3775
kenneth :: 3776
definite :: 3777
darker :: 3778
laurel :: 3779
intent :: 3780
1950's :: 3781
returned :: 3782
superhero :: 3783
sloppy :: 3784
cried :: 3785
worried :: 3786
childish :: 3787
shadows :: 3788
craig :: 3789
cruise :: 3790
hysterical :: 3791
imagined :: 3792
reasonable :: 3793
editor :: 3794
ah :: 3795
birds :: 3796
horrid :: 3797
areas :: 3798
wicked :: 3799
gentle :: 3800
wannabe :: 3801
alexander :: 3802
thick :: 3803
contrary :: 3804
joey :: 3805
empire :: 3806
connect :: 3807
discovery :: 3808
unbearable :: 3809
tortured :: 3810
screams :: 3811
fever :: 3812
unbelievably :: 3813
1930s :: 3814
disc :: 3815
99 :: 3816
load :: 3817
heroic :: 3818
absence :: 3819
reached :: 3820
ho :: 3821
choreography :: 3822
triumph :: 3823
complain :: 3824
annie :: 3825
broad :: 3826
improved :: 3827
concerning :: 3828
brazil :: 3829
movements :: 3830
2003 :: 3831
2004 :: 3832
dave :: 3833
folk :: 3834
eve :: 3835
purple :: 3836
commercials :: 3837
futuristic :: 3838
vicious :: 3839
gray :: 3840
freak :: 3841
threat :: 3842
cusack :: 3843
extended :: 3844
citizen :: 3845
stole :: 3846
anyways :: 3847
glenn :: 3848
existed :: 3849
cheek :: 3850
broadcast :: 3851
photographer :: 3852
translation :: 3853
arrive :: 3854
differences :: 3855
displays :: 3856
critic :: 3857
slave :: 3858
landscape :: 3859
occurred :: 3860
builds :: 3861
drawing :: 3862
incident :: 3863
warren :: 3864
burned :: 3865
involvement :: 3866
styles :: 3867
bathroom :: 3868
machines :: 3869
narrator :: 3870
antics :: 3871
he'll :: 3872
fisher :: 3873
swear :: 3874
australia :: 3875
matthew :: 3876
resembles :: 3877
lily :: 3878
overrated :: 3879
currently :: 3880
symbolism :: 3881
ought :: 3882
bare :: 3883
audio :: 3884
web :: 3885
farm :: 3886
contained :: 3887
greek :: 3888
affected :: 3889
blend :: 3890
q :: 3891
recognized :: 3892
duo :: 3893
genres :: 3894
population :: 3895
carrie :: 3896
ranks :: 3897
demands :: 3898
we'll :: 3899
abc :: 3900
prom :: 3901
altogether :: 3902
superficial :: 3903
kitchen :: 3904
pseudo :: 3905
sunshine :: 3906
sadness :: 3907
secrets :: 3908
bone :: 3909
website :: 3910
receive :: 3911
popcorn :: 3912
threw :: 3913
craft :: 3914
enjoys :: 3915
occur :: 3916
twelve :: 3917
block :: 3918
girl's :: 3919
proceedings :: 3920
dynamic :: 3921
daring :: 3922
swedish :: 3923
argue :: 3924
bite :: 3925
wolf :: 3926
adequate :: 3927
investigate :: 3928
harder :: 3929
ruth :: 3930
ridiculously :: 3931
tap :: 3932
dinosaurs :: 3933
hugh :: 3934
synopsis :: 3935
beats :: 3936
carrey :: 3937
explosion :: 3938
foul :: 3939
merit :: 3940
suited :: 3941
holy :: 3942
staged :: 3943
journalist :: 3944
pretend :: 3945
composed :: 3946
cagney :: 3947
robots :: 3948
giallo :: 3949
aging :: 3950
fay :: 3951
sadistic :: 3952
engaged :: 3953
escaped :: 3954
juvenile :: 3955
rambo :: 3956
ireland :: 3957
conversations :: 3958
thugs :: 3959
modesty :: 3960
selfish :: 3961
margaret :: 3962
dialogues :: 3963
ease :: 3964
cameras :: 3965
tame :: 3966
leg :: 3967
rural :: 3968
comfortable :: 3969
nazis :: 3970
clothing :: 3971
innovative :: 3972
terry :: 3973
thrill :: 3974
2nd :: 3975
dancers :: 3976
brosnan :: 3977
explosions :: 3978
bin :: 3979
rage :: 3980
overwhelming :: 3981
jazz :: 3982
vivid :: 3983
coherent :: 3984
bullet :: 3985
odds :: 3986
mountains :: 3987
kidding :: 3988
versus :: 3989
lit :: 3990
offering :: 3991
mother's :: 3992
trio :: 3993
newspaper :: 3994
pulp :: 3995
ellen :: 3996
dawson :: 3997
bird :: 3998
buddies :: 3999
combat :: 4000
dracula :: 4001
lol :: 4002
grab :: 4003
orders :: 4004
staff :: 4005
nearby :: 4006
cats :: 4007
wealth :: 4008
unpleasant :: 4009
staying :: 4010
devoted :: 4011
centered :: 4012
errors :: 4013
disturbed :: 4014
bell :: 4015
atlantis :: 4016
snake :: 4017
felix :: 4018
damage :: 4019
clint :: 4020
lust :: 4021
groups :: 4022
banned :: 4023
blowing :: 4024
fighter :: 4025
removed :: 4026
react :: 4027
conventional :: 4028
kapoor :: 4029
intrigue :: 4030
possessed :: 4031
cringe :: 4032
eyre :: 4033
liking :: 4034
implausible :: 4035
philosophy :: 4036
producing :: 4037
abilities :: 4038
seventies :: 4039
bang :: 4040
murderous :: 4041
deliberately :: 4042
gandhi :: 4043
tommy :: 4044
meaningless :: 4045
subjects :: 4046
lips :: 4047
ingredients :: 4048
mildred :: 4049
perry :: 4050
warming :: 4051
causing :: 4052
possibility :: 4053
detailed :: 4054
walker :: 4055
garden :: 4056
prostitute :: 4057
nightmares :: 4058
cameron :: 4059
flop :: 4060
influenced :: 4061
spare :: 4062
unwatchable :: 4063
undoubtedly :: 4064
celluloid :: 4065
relies :: 4066
resemblance :: 4067
neo :: 4068
parent :: 4069
falk :: 4070
uneven :: 4071
unintentional :: 4072
eccentric :: 4073
mistaken :: 4074
distracting :: 4075
careers :: 4076
yesterday :: 4077
forbidden :: 4078
panic :: 4079
crack :: 4080
brains :: 4081
highest :: 4082
occasion :: 4083
signs :: 4084
focusing :: 4085
hollow :: 4086
explored :: 4087
aid :: 4088
cary :: 4089
scheme :: 4090
shine :: 4091
it'll :: 4092
kirk :: 4093
bedroom :: 4094
satisfied :: 4095
rat :: 4096
passes :: 4097
survival :: 4098
coffee :: 4099
furthermore :: 4100
primary :: 4101
succeeded :: 4102
politically :: 4103
pays :: 4104
apes :: 4105
stiller :: 4106
dating :: 4107
defeat :: 4108
sport :: 4109
catches :: 4110
mickey :: 4111
clown :: 4112
roman :: 4113
discuss :: 4114
karen :: 4115
clumsy :: 4116
chaos :: 4117
financial :: 4118
official :: 4119
trees :: 4120
explaining :: 4121
models :: 4122
spirits :: 4123
carl :: 4124
jeffrey :: 4125
duty :: 4126
whale :: 4127
funeral :: 4128
secondly :: 4129
sentence :: 4130
2007 :: 4131
classes :: 4132
sidekick :: 4133
tracks :: 4134
props :: 4135
travels :: 4136
flies :: 4137
remarkably :: 4138
smaller :: 4139
wallace :: 4140
awake :: 4141
1996 :: 4142
brady :: 4143
blatant :: 4144
decisions :: 4145
afford :: 4146
notion :: 4147
recorded :: 4148
glorious :: 4149
enterprise :: 4150
maggie :: 4151
consistently :: 4152
toys :: 4153
offended :: 4154
officers :: 4155
danes :: 4156
backdrop :: 4157
beneath :: 4158
masters :: 4159
measure :: 4160
endings :: 4161
doomed :: 4162
mysteries :: 4163
lifestyle :: 4164
houses :: 4165
portion :: 4166
primarily :: 4167
satan :: 4168
hates :: 4169
devoid :: 4170
impress :: 4171
outer :: 4172
generic :: 4173
dutch :: 4174
punk :: 4175
lyrics :: 4176
yellow :: 4177
eastwood :: 4178
exotic :: 4179
represent :: 4180
instant :: 4181
desperation :: 4182
mixture :: 4183
settle :: 4184
frustration :: 4185
unfolds :: 4186
goodness :: 4187
wives :: 4188
directs :: 4189
fetched :: 4190
ape :: 4191
cheating :: 4192
dozens :: 4193
rebel :: 4194
cuba :: 4195
paulie :: 4196
enormous :: 4197
revolutionary :: 4198
hints :: 4199
shelf :: 4200
brooklyn :: 4201
florida :: 4202
dances :: 4203
motives :: 4204
destiny :: 4205
1999 :: 4206
donna :: 4207
hardcore :: 4208
mill :: 4209
wrestling :: 4210
subtlety :: 4211
forty :: 4212
describes :: 4213
drops :: 4214
blake :: 4215
stinker :: 4216
doll :: 4217
painted :: 4218
fond :: 4219
linda :: 4220
principal :: 4221
rank :: 4222
ideal :: 4223
kennedy :: 4224
hammer :: 4225
montage :: 4226
hollywood's :: 4227
tie :: 4228
disjointed :: 4229
3rd :: 4230
reaches :: 4231
amy :: 4232
immensely :: 4233
ginger :: 4234
judging :: 4235
companion :: 4236
communist :: 4237
urge :: 4238
winds :: 4239
developing :: 4240
trailers :: 4241
cliff :: 4242
lawrence :: 4243
stellar :: 4244
topless :: 4245
circle :: 4246
surviving :: 4247
avoided :: 4248
relations :: 4249
bold :: 4250
hideous :: 4251
voight :: 4252
closet :: 4253
et :: 4254
surfing :: 4255
melting :: 4256
soccer :: 4257
edie :: 4258
matches :: 4259
backgrounds :: 4260
planned :: 4261
enemies :: 4262
advance :: 4263
bull :: 4264
authority :: 4265
crush :: 4266
outfit :: 4267
emphasis :: 4268
method :: 4269
terrorist :: 4270
senseless :: 4271
pig :: 4272
uwe :: 4273
simplistic :: 4274
benefit :: 4275
adorable :: 4276
eighties :: 4277
ruthless :: 4278
godzilla :: 4279
blew :: 4280
countryside :: 4281
specifically :: 4282
wont :: 4283
performer :: 4284
hbo :: 4285
traveling :: 4286
todd :: 4287
practice :: 4288
diane :: 4289
fix :: 4290
faster :: 4291
1980 :: 4292
commented :: 4293
sh :: 4294
loyal :: 4295
saga :: 4296
ties :: 4297
disappear :: 4298
awe :: 4299
earned :: 4300
buff :: 4301
rick :: 4302
loads :: 4303
link :: 4304
angeles :: 4305
corruption :: 4306
forms :: 4307
menace :: 4308
miserable :: 4309
claimed :: 4310
vast :: 4311
coach :: 4312
divorce :: 4313
hal :: 4314
gadget :: 4315
chorus :: 4316
limits :: 4317
cure :: 4318
introduces :: 4319
cards :: 4320
solo :: 4321
blues :: 4322
splatter :: 4323
april :: 4324
endure :: 4325
riveting :: 4326
dedicated :: 4327
tender :: 4328
winters :: 4329
illogical :: 4330
choreographed :: 4331
disappeared :: 4332
unsettling :: 4333
waters :: 4334
guessed :: 4335
lemmon :: 4336
involve :: 4337
transformation :: 4338
depressed :: 4339
rooms :: 4340
lasted :: 4341
displayed :: 4342
weakest :: 4343
leonard :: 4344
philosophical :: 4345
racial :: 4346
interaction :: 4347
arrogant :: 4348
tag :: 4349
rocket :: 4350
similarities :: 4351
hurts :: 4352
thoughtful :: 4353
realizing :: 4354
harvey :: 4355
justify :: 4356
hook :: 4357
survivors :: 4358
represented :: 4359
pot :: 4360
possibilities :: 4361
wore :: 4362
disappoint :: 4363
voiced :: 4364
kicked :: 4365
abysmal :: 4366
hamilton :: 4367
buffs :: 4368
safety :: 4369
widow :: 4370
ears :: 4371
nomination :: 4372
trashy :: 4373
honesty :: 4374
stereotype :: 4375
severe :: 4376
formulaic :: 4377
moody :: 4378
similarly :: 4379
stress :: 4380
pan :: 4381
chased :: 4382
isolated :: 4383
blond :: 4384
stinks :: 4385
mario :: 4386
passionate :: 4387
finger :: 4388
shirley :: 4389
march :: 4390
hank :: 4391
improve :: 4392
mann :: 4393
understandable :: 4394
characters' :: 4395
considerable :: 4396
scope :: 4397
holly :: 4398
diana :: 4399
grasp :: 4400
command :: 4401
solely :: 4402
'em :: 4403
concern :: 4404
treats :: 4405
akshay :: 4406
promised :: 4407
colonel :: 4408
jonathan :: 4409
faults :: 4410
helicopter :: 4411
inventive :: 4412
sounding :: 4413
quotes :: 4414
trained :: 4415
switch :: 4416
celebrity :: 4417
tad :: 4418
swimming :: 4419
orson :: 4420
education :: 4421
aids :: 4422
nail :: 4423
judy :: 4424
cg :: 4425
user :: 4426
nervous :: 4427
nostalgic :: 4428
daddy :: 4429
alert :: 4430
amanda :: 4431
facing :: 4432
comparing :: 4433
unhappy :: 4434
preview :: 4435
report :: 4436
bonus :: 4437
purchase :: 4438
chess :: 4439
wet :: 4440
lately :: 4441
horrifying :: 4442
agrees :: 4443
thru :: 4444
dolls :: 4445
cinematographer :: 4446
ignorant :: 4447
species :: 4448
seed :: 4449
consistent :: 4450
downhill :: 4451
corporate :: 4452
photos :: 4453
confidence :: 4454
letters :: 4455
berlin :: 4456
dinosaur :: 4457
rotten :: 4458
taught :: 4459
fooled :: 4460
laws :: 4461
nicholson :: 4462
namely :: 4463
shake :: 4464
waited :: 4465
wished :: 4466
embarrassment :: 4467
everyone's :: 4468
boot :: 4469
pretending :: 4470
reaching :: 4471
someone's :: 4472
transfer :: 4473
sits :: 4474
armed :: 4475
del :: 4476
dub :: 4477
defend :: 4478
hart :: 4479
35 :: 4480
constructed :: 4481
mall :: 4482
poetic :: 4483
motivations :: 4484
inane :: 4485
behave :: 4486
tonight :: 4487
staring :: 4488
humble :: 4489
snl :: 4490
elephant :: 4491
agents :: 4492
oz :: 4493
grandfather :: 4494
writes :: 4495
relation :: 4496
hop :: 4497
delivering :: 4498
fonda :: 4499
edgar :: 4500
cave :: 4501
artificial :: 4502
grinch :: 4503
sappy :: 4504
prize :: 4505
1972 :: 4506
useful :: 4507
buildings :: 4508
li :: 4509
cake :: 4510
eager :: 4511
closest :: 4512
suitable :: 4513
raising :: 4514
destroying :: 4515
combine :: 4516
beatty :: 4517
pants :: 4518
cleverly :: 4519
ballet :: 4520
convincingly :: 4521
porno :: 4522
1990 :: 4523
miike :: 4524
affect :: 4525
engage :: 4526
cd :: 4527
conservative :: 4528
wound :: 4529
arrived :: 4530
stevens :: 4531
alcoholic :: 4532
valuable :: 4533
ya :: 4534
reads :: 4535
scottish :: 4536
elegant :: 4537
vegas :: 4538
chest :: 4539
charlotte :: 4540
climactic :: 4541
tiresome :: 4542
z :: 4543
conflicts :: 4544
babe :: 4545
vengeance :: 4546
square :: 4547
bath :: 4548
secretly :: 4549
airport :: 4550
campbell :: 4551
kingdom :: 4552
september :: 4553
inferior :: 4554
1968 :: 4555
latin :: 4556
plant :: 4557
button :: 4558
museum :: 4559
maintain :: 4560
wrapped :: 4561
kicking :: 4562
cheated :: 4563
global :: 4564
robbery :: 4565
virginia :: 4566
wells :: 4567
waves :: 4568
stilted :: 4569
blunt :: 4570
lena :: 4571
boom :: 4572
access :: 4573
raymond :: 4574
1960s :: 4575
catching :: 4576
nicholas :: 4577
yelling :: 4578
scarecrow :: 4579
beliefs :: 4580
paranoia :: 4581
christians :: 4582
vice :: 4583
jumped :: 4584
lay :: 4585
iron :: 4586
steel :: 4587
lowest :: 4588
reflect :: 4589
closed :: 4590
mummy :: 4591
transition :: 4592
advertising :: 4593
vulnerable :: 4594
abusive :: 4595
1970's :: 4596
spoke :: 4597
plight :: 4598
mars :: 4599
spread :: 4600
adams :: 4601
wizard :: 4602
poetry :: 4603
im :: 4604
sandra :: 4605
germans :: 4606
pokemon :: 4607
progresses :: 4608
70 :: 4609
00 :: 4610
hung :: 4611
questionable :: 4612
remarks :: 4613
airplane :: 4614
centers :: 4615
potentially :: 4616
bottle :: 4617
chicago :: 4618
guarantee :: 4619
couples :: 4620
messed :: 4621
catchy :: 4622
slick :: 4623
gangsters :: 4624
misery :: 4625
blade :: 4626
designs :: 4627
construction :: 4628
ethan :: 4629
desired :: 4630
miracle :: 4631
carradine :: 4632
firstly :: 4633
scores :: 4634
wandering :: 4635
greedy :: 4636
recognition :: 4637
understated :: 4638
restored :: 4639
complexity :: 4640
madonna :: 4641
attitudes :: 4642
rendition :: 4643
hunters :: 4644
intentionally :: 4645
experiments :: 4646
ruby :: 4647
alongside :: 4648
vaguely :: 4649
inappropriate :: 4650
copies :: 4651
operation :: 4652
brutally :: 4653
taxi :: 4654
amounts :: 4655
stooges :: 4656
joined :: 4657
pearl :: 4658
demand :: 4659
crocodile :: 4660
depicts :: 4661
purchased :: 4662
acid :: 4663
myers :: 4664
exploration :: 4665
advise :: 4666
illegal :: 4667
balls :: 4668
king's :: 4669
gundam :: 4670
disney's :: 4671
gender :: 4672
lengthy :: 4673
survived :: 4674
hopper :: 4675
niro :: 4676
advanced :: 4677
simplicity :: 4678
bela :: 4679
parallel :: 4680
ocean :: 4681
slaughter :: 4682
rising :: 4683
witnesses :: 4684
chicks :: 4685
streep :: 4686
visible :: 4687
nostalgia :: 4688
arguably :: 4689
careful :: 4690
intimate :: 4691
online :: 4692
floating :: 4693
rubber :: 4694
june :: 4695
illness :: 4696
resources :: 4697
khan :: 4698
jaw :: 4699
newly :: 4700
witches :: 4701
showcase :: 4702
signed :: 4703
opinions :: 4704
dust :: 4705
eaten :: 4706
civilization :: 4707
shelley :: 4708
incomprehensible :: 4709
invasion :: 4710
lee's :: 4711
monkeys :: 4712
resort :: 4713
literature :: 4714
junior :: 4715
likewise :: 4716
homosexual :: 4717
family's :: 4718
viewings :: 4719
sue :: 4720
wisdom :: 4721
matched :: 4722
amitabh :: 4723
edition :: 4724
witnessed :: 4725
visits :: 4726
mistress :: 4727
1983 :: 4728
demented :: 4729
basketball :: 4730
neighbors :: 4731
macy :: 4732
fascinated :: 4733
dreary :: 4734
suspicious :: 4735
accompanied :: 4736
worn :: 4737
mail :: 4738
challenging :: 4739
doom :: 4740
ensues :: 4741
manipulative :: 4742
robinson :: 4743
classical :: 4744
olivier :: 4745
agreed :: 4746
appreciation :: 4747
franco :: 4748
montana :: 4749
troops :: 4750
capturing :: 4751
alternate :: 4752
bands :: 4753
twilight :: 4754
ridden :: 4755
responsibility :: 4756
proceeds :: 4757
chapter :: 4758
jenny :: 4759
prisoners :: 4760
pops :: 4761
analysis :: 4762
subplots :: 4763
lively :: 4764
nuts :: 4765
prisoner :: 4766
incompetent :: 4767
damon :: 4768
sellers :: 4769
mayor :: 4770
rats :: 4771
simpson :: 4772
90s :: 4773
persons :: 4774
feed :: 4775
descent :: 4776
reel :: 4777
bay :: 4778
assault :: 4779
losers :: 4780
widely :: 4781
rabbit :: 4782
smiling :: 4783
relatives :: 4784
excessive :: 4785
defined :: 4786
satisfy :: 4787
solution :: 4788
legal :: 4789
molly :: 4790
arrival :: 4791
overacting :: 4792
equivalent :: 4793
iran :: 4794
pit :: 4795
masterful :: 4796
capital :: 4797
richardson :: 4798
compelled :: 4799
plausible :: 4800
stale :: 4801
scrooge :: 4802
cities :: 4803
francis :: 4804
enthusiasm :: 4805
lone :: 4806
parties :: 4807
tomatoes :: 4808
channels :: 4809
hilariously :: 4810
rocky :: 4811
crucial :: 4812
dropping :: 4813
unit :: 4814
waitress :: 4815
domestic :: 4816
attorney :: 4817
bakshi :: 4818
serving :: 4819
wrap :: 4820
jaws :: 4821
historically :: 4822
3d :: 4823
defense :: 4824
hello :: 4825
greed :: 4826
1973 :: 4827
priceless :: 4828
sincere :: 4829
warmth :: 4830
paltrow :: 4831
gerard :: 4832
tends :: 4833
god's :: 4834
patients :: 4835
creep :: 4836
counter :: 4837
dalton :: 4838
kay :: 4839
whats :: 4840
louise :: 4841
peoples :: 4842
exceptionally :: 4843
nyc :: 4844
pal :: 4845
seeks :: 4846
terrorists :: 4847
lumet :: 4848
morris :: 4849
ninja :: 4850
randomly :: 4851
frequent :: 4852
despair :: 4853
irrelevant :: 4854
dressing :: 4855
pursuit :: 4856
prequel :: 4857
creativity :: 4858
imitation :: 4859
bumbling :: 4860
hyde :: 4861
property :: 4862
muslim :: 4863
wishing :: 4864
richards :: 4865
bargain :: 4866
50s :: 4867
creator :: 4868
calm :: 4869
bacall :: 4870
gabriel :: 4871
mentioning :: 4872
rangers :: 4873
methods :: 4874
earl :: 4875
royal :: 4876
butler :: 4877
justin :: 4878
psychic :: 4879
chooses :: 4880
belong :: 4881
der :: 4882
photo :: 4883
polanski :: 4884
mundane :: 4885
specially :: 4886
mighty :: 4887
homer :: 4888
ear :: 4889
masterpieces :: 4890
generated :: 4891
leo :: 4892
improvement :: 4893
poem :: 4894
ham :: 4895
cliche :: 4896
marty :: 4897
caliber :: 4898
mentions :: 4899
minimum :: 4900
showdown :: 4901
borrowed :: 4902
elm :: 4903
icon :: 4904
brenda :: 4905
polished :: 4906
1984 :: 4907
mechanical :: 4908
overlook :: 4909
loaded :: 4910
map :: 4911
recording :: 4912
craven :: 4913
tiger :: 4914
roth :: 4915
awfully :: 4916
suffice :: 4917
troubles :: 4918
introduce :: 4919
equipment :: 4920
ashley :: 4921
wendy :: 4922
pamela :: 4923
empathy :: 4924
phantom :: 4925
betty :: 4926
resident :: 4927
unreal :: 4928
ruins :: 4929
performs :: 4930
promises :: 4931
monk :: 4932
iraq :: 4933
hippie :: 4934
purposes :: 4935
marketing :: 4936
angela :: 4937
keith :: 4938
sink :: 4939
gifted :: 4940
opportunities :: 4941
garbo :: 4942
assigned :: 4943
feminist :: 4944
household :: 4945
wacky :: 4946
alfred :: 4947
absent :: 4948
sneak :: 4949
popularity :: 4950
trail :: 4951
inducing :: 4952
moronic :: 4953
wounded :: 4954
receives :: 4955
willis :: 4956
unseen :: 4957
stretched :: 4958
fulci :: 4959
unaware :: 4960
dimension :: 4961
dolph :: 4962
definition :: 4963
testament :: 4964
educational :: 4965
survivor :: 4966
attend :: 4967
clip :: 4968
contest :: 4969
petty :: 4970
13th :: 4971
christy :: 4972
respected :: 4973
resist :: 4974
year's :: 4975
album :: 4976
expressed :: 4977
randy :: 4978
quit :: 4979
phony :: 4980
unoriginal :: 4981
punishment :: 4982
activities :: 4983
suspend :: 4984
rolled :: 4985
eastern :: 4986
1933 :: 4987
instinct :: 4988
distinct :: 4989
championship :: 4990
tech :: 4991
doubts :: 4992
interests :: 4993
exposure :: 4994
travesty :: 4995
israel :: 4996
sixties :: 4997
pink :: 4998
orange :: 4999
time: 1.54 s (started: 2021-11-21 15:52:40 +00:00)
'the', 'and' , 'a' are most frequent used words. Here we are not using stopwords to remove the words from the sentences. These words will carry meaning to the sentiments and hence will not be removed before processing.
Adding padding to the data with maximum length of 300. Here we are pre padding the words and truncating the words in sentences which are having more the 300 word length
#Define maximum number of words to consider in each review
max_review_length = 300
#Pad training and test reviews
X_train = tf.keras.preprocessing.sequence.pad_sequences(X_train,
maxlen=max_review_length,
padding='pre',
truncating='post')
X_val = tf.keras.preprocessing.sequence.pad_sequences(X_val,
maxlen=max_review_length,
padding='pre',
truncating='post')
X_test = tf.keras.preprocessing.sequence.pad_sequences(X_test,
maxlen=max_review_length,
padding='pre',
truncating='post')
X_unseen_data = tf.keras.preprocessing.sequence.pad_sequences(X_unseen_data,
maxlen=max_review_length,
padding='pre',
truncating='post')
time: 1.78 s (started: 2021-11-21 15:52:41 +00:00)
We are building the model with simple LSTM layer and dense layer as sigmoid for the output since it is a binary classification and we are using binary_crossentropy as loss.
#Initialize model
tf.keras.backend.clear_session()
model = tf.keras.Sequential()
time: 803 ms (started: 2021-11-21 15:52:43 +00:00)
Here we are generating the embedding with size of 50 for each review. Here the embedding layer will be trained and hence we will see more number of parameters to be trained
model.add(tf.keras.layers.Embedding(desired_vocab_size + 1, #Vocablury size
50, #Embedding size
input_length=max_review_length) #Number of words in each review
)
model.output
<KerasTensor: shape=(None, 300, 50) dtype=float32 (created by layer 'embedding')>
time: 75.3 ms (started: 2021-11-21 15:52:44 +00:00)
define dropout and LSTM. LSTM size of cell and hidden state of 128.
model.add(tf.keras.layers.Dropout(0.2))
model.add(tf.keras.layers.LSTM(128)) #RNN State - size of cell state and hidden state
time: 1.5 s (started: 2021-11-21 15:52:44 +00:00)
model.output
<KerasTensor: shape=(None, 128) dtype=float32 (created by layer 'lstm')>
time: 6.19 ms (started: 2021-11-21 15:52:46 +00:00)
using sigmoid as activation function. This is the output layer.
model.add(tf.keras.layers.Dense(1,activation='sigmoid'))
time: 30.6 ms (started: 2021-11-21 15:52:46 +00:00)
#Compile the model
model.compile(optimizer='adam',loss='binary_crossentropy',metrics=['accuracy'])
time: 25.4 ms (started: 2021-11-21 15:52:46 +00:00)
model.summary()
Model: "sequential"
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
embedding (Embedding) (None, 300, 50) 500050
dropout (Dropout) (None, 300, 50) 0
lstm (LSTM) (None, 128) 91648
dense (Dense) (None, 1) 129
=================================================================
Total params: 591,827
Trainable params: 591,827
Non-trainable params: 0
_________________________________________________________________
time: 2.4 ms (started: 2021-11-21 15:52:46 +00:00)
As we can observe, the number of Non-trainable params are 0 and we there are more number of parameters (weights and biases). Most number fo parameters are in emdedding layer when compare with LSTM layer.
history_manual_emdb = model.fit(X_train,y_train,
epochs=10,
batch_size=32,
validation_data=(X_val, y_val))
Epoch 1/10 1094/1094 [==============================] - 27s 21ms/step - loss: 0.4418 - accuracy: 0.7973 - val_loss: 0.3980 - val_accuracy: 0.8322 Epoch 2/10 1094/1094 [==============================] - 22s 20ms/step - loss: 0.2882 - accuracy: 0.8874 - val_loss: 0.3378 - val_accuracy: 0.8597 Epoch 3/10 1094/1094 [==============================] - 22s 20ms/step - loss: 0.2319 - accuracy: 0.9108 - val_loss: 0.3190 - val_accuracy: 0.8776 Epoch 4/10 1094/1094 [==============================] - 22s 20ms/step - loss: 0.1796 - accuracy: 0.9345 - val_loss: 0.3428 - val_accuracy: 0.8688 Epoch 5/10 1094/1094 [==============================] - 22s 20ms/step - loss: 0.1529 - accuracy: 0.9437 - val_loss: 0.4300 - val_accuracy: 0.8650 Epoch 6/10 1094/1094 [==============================] - 22s 20ms/step - loss: 0.1254 - accuracy: 0.9548 - val_loss: 0.4871 - val_accuracy: 0.8429 Epoch 7/10 1094/1094 [==============================] - 22s 20ms/step - loss: 0.1144 - accuracy: 0.9583 - val_loss: 0.4239 - val_accuracy: 0.8550 Epoch 8/10 1094/1094 [==============================] - 22s 20ms/step - loss: 0.0832 - accuracy: 0.9727 - val_loss: 0.4623 - val_accuracy: 0.8628 Epoch 9/10 1094/1094 [==============================] - 22s 20ms/step - loss: 0.0768 - accuracy: 0.9743 - val_loss: 0.4939 - val_accuracy: 0.8498 Epoch 10/10 1094/1094 [==============================] - 22s 20ms/step - loss: 0.0640 - accuracy: 0.9790 - val_loss: 0.5358 - val_accuracy: 0.8622 time: 4min 26s (started: 2021-11-21 15:52:46 +00:00)
scores = model.evaluate(X_test, y_test, verbose=0)
print("Accuracy: %.2f%%" % (scores[1]*100))
Accuracy: 85.82% time: 1.16 s (started: 2021-11-21 15:57:12 +00:00)
As observed in above cell, the ovrall accuracy is around 85% for the test data. Although the model is having high accuracy on the train data, it may be a over fit model since the test accuracy is way below the train accuracy and the same is with validation accuracy.
Checking model performance using the graphs
import matplotlib.pyplot as plt
plt.plot(history_manual_emdb.history['accuracy'])
plt.plot(history_manual_emdb.history['val_accuracy'])
plt.title('model accuracy')
plt.ylabel('accuracy')
plt.xlabel('epoch')
plt.legend(['train', 'validation'], loc='upper left')
plt.show()
time: 226 ms (started: 2021-11-21 15:57:13 +00:00)
# summarize history for loss
plt.plot(history_manual_emdb.history['loss'])
plt.plot(history_manual_emdb.history['val_loss'])
plt.title('model loss')
plt.ylabel('loss')
plt.xlabel('epoch')
plt.legend(['train', 'test'], loc='upper left')
plt.show()
time: 171 ms (started: 2021-11-21 15:57:13 +00:00)
The above model is a over fitting model as it is having large gap between validation and train accuracy and loss. As observed the gap is widening.
We can plan to use pre-trained embedding like Glove. GloVe stands for “Global Vectors”. And as mentioned earlier, GloVe captures both global statistics and local statistics of a corpus, in order to come up with word vectors. GloVe does not rely just on local statistics (local context information of words), but incorporates global statistics (word co-occurrence) to obtain word vectors.
using gensim library and load the glove embedding. The basic idea behind the GloVe word embedding is to derive the relationship between the words from statistics. Unlike the occurrence matrix, the co-occurrence matrix tells you how often a particular word pair occurs together. Each value in the co-occurrence matrix represents a pair of words occurring together.
import gensim.downloader as api
import numpy as np
#Load Glove model (similar to Word2Vec)
glove_model = api.load('glove-wiki-gigaword-50')
#Size of the model
glove_model.vectors.shape
#Initialize embedding matrix for our dataset with 10000+1 rows (1 for padding word)
#and 50 columns (as embedding size is 50)
embedding_matrix = np.zeros((desired_vocab_size + 1, 50))
[==================================================] 100.0% 66.0/66.0MB downloaded time: 47.6 s (started: 2021-11-21 15:57:13 +00:00)
comparing the IMDB words with the global embedding and developing the embeding vector.
for word, i in sorted(word_to_id.items(),key=lambda x:x[1]):
if i > (desired_vocab_size+1):
break
try:
embedding_vector = glove_model[word] #Reading word's embedding from Glove model for a given word
embedding_matrix[i] = embedding_vector
except:
pass
time: 79.5 ms (started: 2021-11-21 15:58:01 +00:00)
Checking random embedding matrix
embedding_matrix[200]
array([ 0.75029999, 0.71622998, -0.27033001, 0.20059 , -0.17008001,
0.68567997, -0.061672 , -0.054638 , -0.86404002, -0.61395001,
-0.11047 , -0.38826999, -0.65705001, -0.51993001, -0.11222 ,
0.35218 , -0.062404 , -0.1367 , -0.74921 , 0.29495999,
0.64600003, -0.045973 , 0.67606997, 0.43915001, -0.11932 ,
-1.25650001, 0.61852998, -0.057893 , 0.11771 , 0.45168999,
3.8217001 , 0.19942001, -0.30559999, -0.3612 , 0.16007 ,
-0.62273997, -0.83082998, -0.032579 , -0.48379999, 0.60626 ,
-0.22847 , -0.073889 , 0.052593 , 0.039626 , -0.72324997,
0.11038 , 0.35692 , -0.26172 , -0.67303002, -0.87037998])
time: 4.55 ms (started: 2021-11-21 15:58:01 +00:00)
Building the model using LSTM layer.
#Initialize model
tf.keras.backend.clear_session()
model_glove_emdb = tf.keras.Sequential()
time: 21.2 ms (started: 2021-11-21 15:58:01 +00:00)
model_glove_emdb.add(tf.keras.layers.Embedding(desired_vocab_size + 1, #Vocablury size
50, #Embedding size
weights=[embedding_matrix],
trainable=False,
input_length=max_review_length) #Number of words in each review
)
time: 15.1 ms (started: 2021-11-21 15:58:01 +00:00)
Defining LSTM with cell and hidden state length as 128
model_glove_emdb.add(tf.keras.layers.Dropout(0.3))
model_glove_emdb.add(tf.keras.layers.LSTM(128)) #RNN State - size of cell state and hidden state
model_glove_emdb.add(tf.keras.layers.Dense(1,activation='sigmoid'))
time: 314 ms (started: 2021-11-21 15:58:01 +00:00)
#Compile the model
model_glove_emdb.compile(optimizer='adam',loss='binary_crossentropy',metrics=['accuracy'])
time: 7.74 ms (started: 2021-11-21 15:58:01 +00:00)
model_glove_emdb.summary()
Model: "sequential"
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
embedding (Embedding) (None, 300, 50) 500050
dropout (Dropout) (None, 300, 50) 0
lstm (LSTM) (None, 128) 91648
dense (Dense) (None, 1) 129
=================================================================
Total params: 591,827
Trainable params: 91,777
Non-trainable params: 500,050
_________________________________________________________________
time: 3.44 ms (started: 2021-11-21 15:58:02 +00:00)
As we can see in above table, the entire embedding layer is made as non trainable and in a way we are using transfer learning and this particular action is helping us to improve accuracy.
history = model_glove_emdb.fit(X_train,y_train,
epochs=30,
batch_size=32,
validation_data=(X_val, y_val))
Epoch 1/30 1094/1094 [==============================] - 24s 20ms/step - loss: 0.6390 - accuracy: 0.6375 - val_loss: 0.6320 - val_accuracy: 0.6494 Epoch 2/30 1094/1094 [==============================] - 21s 19ms/step - loss: 0.5206 - accuracy: 0.7353 - val_loss: 0.4160 - val_accuracy: 0.8098 Epoch 3/30 1094/1094 [==============================] - 21s 19ms/step - loss: 0.4219 - accuracy: 0.8063 - val_loss: 0.3780 - val_accuracy: 0.8286 Epoch 4/30 1094/1094 [==============================] - 21s 19ms/step - loss: 0.3977 - accuracy: 0.8204 - val_loss: 0.3571 - val_accuracy: 0.8443 Epoch 5/30 1094/1094 [==============================] - 21s 19ms/step - loss: 0.3801 - accuracy: 0.8287 - val_loss: 0.3352 - val_accuracy: 0.8502 Epoch 6/30 1094/1094 [==============================] - 21s 19ms/step - loss: 0.3662 - accuracy: 0.8349 - val_loss: 0.3225 - val_accuracy: 0.8587 Epoch 7/30 1094/1094 [==============================] - 21s 20ms/step - loss: 0.3469 - accuracy: 0.8473 - val_loss: 0.3118 - val_accuracy: 0.8628 Epoch 8/30 1094/1094 [==============================] - 21s 20ms/step - loss: 0.3349 - accuracy: 0.8539 - val_loss: 0.3027 - val_accuracy: 0.8639 Epoch 9/30 1094/1094 [==============================] - 22s 20ms/step - loss: 0.3229 - accuracy: 0.8598 - val_loss: 0.2988 - val_accuracy: 0.8671 Epoch 10/30 1094/1094 [==============================] - 21s 20ms/step - loss: 0.3137 - accuracy: 0.8621 - val_loss: 0.2931 - val_accuracy: 0.8748 Epoch 11/30 1094/1094 [==============================] - 21s 20ms/step - loss: 0.3040 - accuracy: 0.8680 - val_loss: 0.2992 - val_accuracy: 0.8701 Epoch 12/30 1094/1094 [==============================] - 21s 19ms/step - loss: 0.2909 - accuracy: 0.8760 - val_loss: 0.2892 - val_accuracy: 0.8741 Epoch 13/30 1094/1094 [==============================] - 21s 20ms/step - loss: 0.2836 - accuracy: 0.8796 - val_loss: 0.2861 - val_accuracy: 0.8740 Epoch 14/30 1094/1094 [==============================] - 21s 19ms/step - loss: 0.2726 - accuracy: 0.8828 - val_loss: 0.2821 - val_accuracy: 0.8770 Epoch 15/30 1094/1094 [==============================] - 21s 20ms/step - loss: 0.2662 - accuracy: 0.8862 - val_loss: 0.2873 - val_accuracy: 0.8784 Epoch 16/30 1094/1094 [==============================] - 21s 20ms/step - loss: 0.2573 - accuracy: 0.8907 - val_loss: 0.3152 - val_accuracy: 0.8611 Epoch 17/30 1094/1094 [==============================] - 21s 20ms/step - loss: 0.2494 - accuracy: 0.8945 - val_loss: 0.2842 - val_accuracy: 0.8780 Epoch 18/30 1094/1094 [==============================] - 21s 19ms/step - loss: 0.2438 - accuracy: 0.8981 - val_loss: 0.2803 - val_accuracy: 0.8821 Epoch 19/30 1094/1094 [==============================] - 21s 20ms/step - loss: 0.2351 - accuracy: 0.9010 - val_loss: 0.2837 - val_accuracy: 0.8820 Epoch 20/30 1094/1094 [==============================] - 21s 20ms/step - loss: 0.2311 - accuracy: 0.9032 - val_loss: 0.2850 - val_accuracy: 0.8814 Epoch 21/30 1094/1094 [==============================] - 21s 20ms/step - loss: 0.2248 - accuracy: 0.9059 - val_loss: 0.3018 - val_accuracy: 0.8756 Epoch 22/30 1094/1094 [==============================] - 22s 20ms/step - loss: 0.2190 - accuracy: 0.9089 - val_loss: 0.2956 - val_accuracy: 0.8815 Epoch 23/30 1094/1094 [==============================] - 22s 20ms/step - loss: 0.2147 - accuracy: 0.9095 - val_loss: 0.2995 - val_accuracy: 0.8808 Epoch 24/30 1094/1094 [==============================] - 21s 20ms/step - loss: 0.2058 - accuracy: 0.9149 - val_loss: 0.3026 - val_accuracy: 0.8779 Epoch 25/30 1094/1094 [==============================] - 22s 20ms/step - loss: 0.2066 - accuracy: 0.9135 - val_loss: 0.2963 - val_accuracy: 0.8803 Epoch 26/30 1094/1094 [==============================] - 22s 20ms/step - loss: 0.1992 - accuracy: 0.9166 - val_loss: 0.3032 - val_accuracy: 0.8821 Epoch 27/30 1094/1094 [==============================] - 21s 20ms/step - loss: 0.1954 - accuracy: 0.9193 - val_loss: 0.3042 - val_accuracy: 0.8780 Epoch 28/30 1094/1094 [==============================] - 21s 20ms/step - loss: 0.1897 - accuracy: 0.9220 - val_loss: 0.3070 - val_accuracy: 0.8803 Epoch 29/30 1094/1094 [==============================] - 21s 20ms/step - loss: 0.1908 - accuracy: 0.9213 - val_loss: 0.2967 - val_accuracy: 0.8812 Epoch 30/30 1094/1094 [==============================] - 21s 20ms/step - loss: 0.1838 - accuracy: 0.9242 - val_loss: 0.3397 - val_accuracy: 0.8678 time: 10min 44s (started: 2021-11-21 15:58:02 +00:00)
scores = model_glove_emdb.evaluate(X_test, y_test, verbose=0)
print("Accuracy: %.2f%%" % (scores[1]*100))
Accuracy: 86.12% time: 1.16 s (started: 2021-11-21 16:08:46 +00:00)
The overall accuracy is aroud 88% and lets check if the model is overfitting model with help of graph
import matplotlib.pyplot as plt
plt.plot(history.history['accuracy'])
plt.plot(history.history['val_accuracy'])
plt.title('model accuracy')
plt.ylabel('accuracy')
plt.xlabel('epoch')
plt.legend(['train', 'validation'], loc='upper left')
plt.show()
time: 240 ms (started: 2021-11-21 16:08:47 +00:00)
# summarize history for loss
plt.plot(history.history['loss'])
plt.plot(history.history['val_loss'])
plt.title('model loss')
plt.ylabel('loss')
plt.xlabel('epoch')
plt.legend(['train', 'test'], loc='upper left')
plt.show()
time: 197 ms (started: 2021-11-21 16:08:48 +00:00)
As we can observe in above graphs, the train and validation accuracy is close to each other and difference in loss between is also less. This model is not a overfitting model as previous one.
We are considering only 50 records among the dataset to get the output and compare them. Here we are having on function for displaying the confusion matrix which will help us evaluate the score of the model.
import seaborn as sns
from sklearn.metrics import confusion_matrix
import matplotlib.pyplot as plt
def print_confusion_matrix(testY, predicted_test_y):
mat = confusion_matrix(testY, predicted_test_y)
plt.figure(figsize=(4, 4))
sns.set()
sns.heatmap(mat.T, square=True, annot=True, fmt='d', cbar=False,
xticklabels=np.unique(testY),
yticklabels=np.unique(testY))
plt.xlabel('true label')
plt.ylabel('predicted label')
plt.show()
time: 244 ms (started: 2021-11-21 16:08:48 +00:00)
#Calculate accuracy on Unseen Dataset
sentiment_unseen = model_glove_emdb.predict(X_unseen_data)
predicted_unseen_y_binary = sentiment_unseen >= 0.5
print_confusion_matrix(y_unseen_data, predicted_unseen_y_binary)
time: 662 ms (started: 2021-11-21 16:08:48 +00:00)
Observation from above graphs are mentioned below
1) There are 15 True negative : True negatives (TN): We predicted no, and they are negative sentiment from viewers of movie.
2) There are 28 True positive. True positives (TP): These are cases in which we predicted yes (they are positive sentiment about the movie), and they are positive sentiment.
3) There are 3 False positive : false positives (FP): We predicted yes, but that particular review was negative. (Also known as a "Type I error.")
4) There are 6 False negative : false negatives (FN): We predicted no, but that particular review was Positive. (Also known as a "Type II error.")
#Calculate accuracy on Test Dataset
sentiment_test = model_glove_emdb.predict(X_test)
predicted_test_y_binary = sentiment_test >= 0.5
print_confusion_matrix(y_test, predicted_test_y_binary)
time: 1.13 s (started: 2021-11-21 16:08:49 +00:00)
Observation from above graphs are mentioned below
1) There are 2044 True negative : True negatives (TN): We predicted no, and they are negative sentiment from viewers of movie.
2) There are 2278 True positive. True positives (TP): These are cases in which we predicted yes (they are positive sentiment about the movie), and they are positive sentiment.
3) There are 161 False positive : false positives (FP): We predicted yes, but that particular review was negative. (Also known as a "Type I error.")
4) There are 526 False negative : false negatives (FN): We predicted no, but that particular review was Positive. (Also known as a "Type II error.")
import pandas as pd
df_IMDB_compare_results = pd.DataFrame()
df_IMDB_compare_results['Actual_Data'] = np.where(y_unseen_data == 1, 'Positive','Negative')
time: 5.35 ms (started: 2021-11-21 16:08:50 +00:00)
df_IMDB_compare_results['Predicted_Data']= np.where(predicted_unseen_y_binary, 'Positive','Negative')
time: 1.99 ms (started: 2021-11-21 16:08:50 +00:00)
Checking few examples of the movie review.
def print_user_review_with_sentiment(index):
id_to_word = {value:key for key,value in word_to_id.items()}
user_review = ' '.join(id_to_word[id] for id in X_unseen_data[index])
print(user_review.replace("<PAD>", "").replace(" ", "").replace(" <UNK>",""))
print(df_IMDB_compare_results.loc[index,'Actual_Data'])
print(df_IMDB_compare_results.loc[index,'Predicted_Data'])
print_user_review_with_sentiment(0)
print()
print_user_review_with_sentiment(1)
rarely does a film capture such intense drama and emotion what makes this film so unsettling is that the drama feels so real it's almost hard to remember that it's only a movie this is by far and alan performance of their careers the film almost feels like a theatrical production the way it is staged and lit the only bad thing about this movie is that it's very difficult to get a copy of it i have yet to see it other than on this truly powerful film deserves a transfer and special edition treatment on dvd it really is that good Positive Positive <UNK> the first time i saw this movie was on a flight between china and los angeles it was a real hoot and made the trip pass with much less than the normal 10 hour flight i tried to locate a copy of it without success until i discovered a copy for sale on ebay having now watched it twice i recommend it as good entertainment my only real criticisms are that the choice of english translation words for the subtitles is sub par even by normal standards also the is little to small into the movie too often and frequently travels too fast to read well Positive Positive time: 39.7 ms (started: 2021-11-21 16:08:50 +00:00)
df_IMDB_compare_results
| Actual_Data | Predicted_Data | |
|---|---|---|
| 0 | Positive | Positive |
| 1 | Positive | Positive |
| 2 | Positive | Positive |
| 3 | Positive | Positive |
| 4 | Negative | Negative |
| 5 | Negative | Positive |
| 6 | Positive | Positive |
| 7 | Positive | Negative |
| 8 | Positive | Positive |
| 9 | Positive | Positive |
| 10 | Negative | Negative |
| 11 | Negative | Negative |
| 12 | Negative | Positive |
| 13 | Positive | Positive |
| 14 | Positive | Positive |
| 15 | Negative | Negative |
| 16 | Negative | Negative |
| 17 | Negative | Positive |
| 18 | Negative | Negative |
| 19 | Positive | Positive |
| 20 | Negative | Negative |
| 21 | Positive | Positive |
| 22 | Positive | Positive |
| 23 | Negative | Positive |
| 24 | Positive | Positive |
| 25 | Positive | Positive |
| 26 | Negative | Negative |
| 27 | Positive | Positive |
| 28 | Positive | Positive |
| 29 | Negative | Negative |
| 30 | Negative | Negative |
| 31 | Positive | Negative |
| 32 | Positive | Positive |
| 33 | Positive | Positive |
| 34 | Positive | Positive |
| 35 | Positive | Positive |
| 36 | Negative | Negative |
| 37 | Positive | Positive |
| 38 | Positive | Positive |
| 39 | Positive | Positive |
| 40 | Positive | Negative |
| 41 | Positive | Positive |
| 42 | Negative | Positive |
| 43 | Negative | Positive |
| 44 | Negative | Negative |
| 45 | Negative | Negative |
| 46 | Positive | Positive |
| 47 | Negative | Negative |
| 48 | Positive | Positive |
| 49 | Positive | Positive |
time: 23.7 ms (started: 2021-11-21 16:08:50 +00:00)
There are only few misclassification observed when comparing the result. Overall the model performance is good and it is providing better results.
The data set provided was inbuilt in tensorflow which is having 50000 records, which is split into test, validation and unseen data for testing and validation purpose. Here we have implemented 2 models. For model #1, we built our own embedding and for model #2 we used glove model.
The model which is built using glove model was having high accuracy and performance was better overall. The model has very less parameter to be trained and it helped avoid overfitting of the model. In general, it is always advised to use the transfer learning where ever possible so as t increase accuracy and reliability on the model. Transfer learning is a machine learning method where a model developed for a task is reused as the starting point for a model on a second task. It is a popular approach in deep learning where pre-trained models are used as the starting point on natural language processing tasks given the vast compute and time resources required to develop neural network models on these problems and from the huge jumps in skill that they provide on related problems. Compared to word2vec, GloVe allows for parallel implementation, which means that it's easier to train over more data. It is believed (GloVe) to combine the benefits of the word2vec skip-gram model in the word analogy tasks, with those of matrix factorization methods exploiting global statistical information.
As we compared the test data, below are some observation from the graphs.There are lot of True negative and True positive. So the model is doing pretty well for predicting negative sentiment from viewers of movie and for predicting positive sentiment from viewers of movie. Here we also observe 179 False positive and 449 False negative these are "Type I error" and "Type II error", for mitigation of these errors either we have to go with other models like bidirectional LSTM model which takes feeds from both forward and backward direction and thus help us to understand the reviews better.
In this project, we used pre-trained embedding - Glove. GloVe stands for “Global Vectors”. GloVe captures both global statistics and local statistics of a corpus, in order to come up with word vectors. GloVe does not rely just on local statistics (local context information of words), but incorporates global statistics (word co-occurrence) to obtain word vectors. This embedding helped us a lot in determining correct review of movie and improved accuracy of the model.
DOMAIN: Social media analytics
CONTEXT: Past studies in Sarcasm Detection mostly make use of Twitter datasets collected using hashtag based supervision but such datasets are noisy in terms of labels and language. Furthermore, many tweets are replies to other tweets and detecting sarcasm in these requires the availability of contextual tweets.In this hands-on project, the goal is to build a model to detect whether a sentence is sarcastic or not, using Bidirectional LSTMs.
DATA DESCRIPTION: The dataset is collected from two news websites, theonion.com and huffingtonpost.com. This new dataset has the following advantages over the existing Twitter datasets: Since news headlines are written by professionals in a formal manner, there are no spelling mistakes and informal usage. This reduces the sparsity and also increases the chance of finding pre-trained embeddings. Furthermore, since the sole purpose of TheOnion is to publish sarcastic news, we get high-quality labels with much less noise as compared to Twitter datasets. Unlike tweets that reply to other tweets, the news headlines obtained are self-contained. This would help us in teasing apart the real sarcastic elements Content: Each record consists of three attributes: is_sarcastic: 1 if the record is sarcastic otherwise 0 headline: the headline of the news article article_link: link to the original news article. Useful in collecting supplementary data Reference: https://github.com/rishabhmisra/News-Headlines-Dataset-For-Sarcasm-Detection
PROJECT OBJECTIVE: Build a sequential NLP classifier which can use input text parameters to determine the customer sentiments. Steps and tasks: [ Total Score: 30 points]
from google.colab import drive
drive.mount('/content/drive')
Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount("/content/drive", force_remount=True).
time: 2.12 ms (started: 2021-11-21 16:08:50 +00:00)
#importing required libraries and converting them to dataframe
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from sklearn.model_selection import train_test_split
import gensim.downloader as api
import numpy as np
import tensorflow as tf
from tensorflow import keras
from tensorflow.keras.preprocessing.text import Tokenizer
from tensorflow.keras.preprocessing.sequence import pad_sequences
time: 20 ms (started: 2021-11-21 16:08:50 +00:00)
#parsing the input Json value
def parseJson(fname):
for line in open(fname, 'r'):
yield eval(line)
time: 1.72 ms (started: 2021-11-21 16:08:50 +00:00)
#reading the Json data as List
data = list(parseJson('/content/drive/MyDrive/MachineLearning/NLP/Project/Week6Project/Sarcasm_Headlines_Dataset.json'))
time: 1.58 s (started: 2021-11-21 16:08:50 +00:00)
#printing lenght of data (as list)
print("Length of the data (as list) : ", len(data))
Length of the data (as list) : 28619 time: 1.04 ms (started: 2021-11-21 16:08:52 +00:00)
df = pd.DataFrame(data)
time: 34.6 ms (started: 2021-11-21 16:08:52 +00:00)
df.sample(10)
| is_sarcastic | headline | article_link | |
|---|---|---|---|
| 24001 | 1 | judge restricts roger stone's travel between f... | https://politics.theonion.com/judge-restricts-... |
| 20790 | 0 | home invasion prompts neighbors to invest in s... | https://www.huffingtonpost.com/entry/home-inva... |
| 7115 | 1 | area man too deep into haircut to start talkin... | https://local.theonion.com/area-man-too-deep-i... |
| 10552 | 1 | report: white house overruled intelligence off... | https://politics.theonion.com/report-white-hou... |
| 26381 | 1 | ap reporter in gaza needs another term for 'bl... | https://www.theonion.com/ap-reporter-in-gaza-n... |
| 8305 | 0 | why this lawyer quit his job to open a nationa... | https://www.huffingtonpost.com/entry/lawyer-na... |
| 19404 | 1 | nation's still-undecided voters: 'help, we can... | https://politics.theonion.com/nation-s-still-u... |
| 2702 | 1 | good news kept from parents out of fear of pro... | https://www.theonion.com/good-news-kept-from-p... |
| 6098 | 1 | area woman worried she's forgetting what heath... | https://entertainment.theonion.com/area-woman-... |
| 22363 | 0 | baby jesus and the war on christmas | https://www.huffingtonpost.com/entry/baby-jesu... |
time: 20.2 ms (started: 2021-11-21 16:08:52 +00:00)
df['is_sarcastic'].value_counts().plot.pie(subplots=True)
array([<matplotlib.axes._subplots.AxesSubplot object at 0x7fb232be0c10>],
dtype=object)
time: 107 ms (started: 2021-11-21 16:08:52 +00:00)
The dataset is having almost equal number of sarcastic and normal headlines. Although it is not exactly balanced dataset.
Lets check frequent word using library
WordCloud is a technique to show which words are the most frequent among the given text.
Word Clouds (also known as wordle, word collage or tag cloud) are visual representations of words that give greater prominence to words that appear more frequently.
from wordcloud import WordCloud, STOPWORDS
# Reference: # https://www.geeksforgeeks.org/generating-word-cloud-python/
def wordcloud_plot(df):
comment_words = ""
stopwords = set(STOPWORDS)
# iterate through the csv file
for val in df.headline:
# typecaste each val to string
val = str(val)
# split the value
tokens = val.split()
# Converts each token into lowercase
for i in range(len(tokens)):
tokens[i] = tokens[i].lower()
comment_words += " ".join(tokens)+" "
wordcloud = WordCloud(width = 800, height = 800,
background_color = "white",
stopwords = stopwords,
min_font_size = 10).generate(comment_words)
# plot the WordCloud image
plt.figure(figsize = (8, 8), facecolor = None)
plt.imshow(wordcloud)
plt.axis("off")
plt.tight_layout(pad = 0)
plt.show()
time: 48.6 ms (started: 2021-11-21 16:08:52 +00:00)
wordcloud_plot(df[['headline']])
time: 4.26 s (started: 2021-11-21 16:08:52 +00:00)
As observed, the above image shows the frequent words used among the given text.
df['LengthOfSentence'] = df["headline"].str.len()
time: 22.2 ms (started: 2021-11-21 16:08:56 +00:00)
df
| is_sarcastic | headline | article_link | LengthOfSentence | |
|---|---|---|---|---|
| 0 | 1 | thirtysomething scientists unveil doomsday clo... | https://www.theonion.com/thirtysomething-scien... | 61 |
| 1 | 0 | dem rep. totally nails why congress is falling... | https://www.huffingtonpost.com/entry/donna-edw... | 79 |
| 2 | 0 | eat your veggies: 9 deliciously different recipes | https://www.huffingtonpost.com/entry/eat-your-... | 49 |
| 3 | 1 | inclement weather prevents liar from getting t... | https://local.theonion.com/inclement-weather-p... | 52 |
| 4 | 1 | mother comes pretty close to using word 'strea... | https://www.theonion.com/mother-comes-pretty-c... | 61 |
| ... | ... | ... | ... | ... |
| 28614 | 1 | jews to celebrate rosh hashasha or something | https://www.theonion.com/jews-to-celebrate-ros... | 44 |
| 28615 | 1 | internal affairs investigator disappointed con... | https://local.theonion.com/internal-affairs-in... | 87 |
| 28616 | 0 | the most beautiful acceptance speech this week... | https://www.huffingtonpost.com/entry/andrew-ah... | 71 |
| 28617 | 1 | mars probe destroyed by orbiting spielberg-gat... | https://www.theonion.com/mars-probe-destroyed-... | 61 |
| 28618 | 1 | dad clarifies this not a food stop | https://www.theonion.com/dad-clarifies-this-no... | 34 |
28619 rows × 4 columns
time: 26.3 ms (started: 2021-11-21 16:08:56 +00:00)
PLoting the number of rows against the length of sentence with bins.
df['LengthOfSentence']
bins= [0,100,200,300]
plt.hist(df['LengthOfSentence'], bins=bins, edgecolor="k")
plt.xticks(bins)
plt.show()
time: 185 ms (started: 2021-11-21 16:08:56 +00:00)
Checking max length available in the headlines column
df['LengthOfSentence'].max()
926
time: 3.29 ms (started: 2021-11-21 16:08:56 +00:00)
processedDataFrame = df[['headline','is_sarcastic']]
time: 4.22 ms (started: 2021-11-21 16:08:56 +00:00)
processedDataFrame
| headline | is_sarcastic | |
|---|---|---|
| 0 | thirtysomething scientists unveil doomsday clo... | 1 |
| 1 | dem rep. totally nails why congress is falling... | 0 |
| 2 | eat your veggies: 9 deliciously different recipes | 0 |
| 3 | inclement weather prevents liar from getting t... | 1 |
| 4 | mother comes pretty close to using word 'strea... | 1 |
| ... | ... | ... |
| 28614 | jews to celebrate rosh hashasha or something | 1 |
| 28615 | internal affairs investigator disappointed con... | 1 |
| 28616 | the most beautiful acceptance speech this week... | 0 |
| 28617 | mars probe destroyed by orbiting spielberg-gat... | 1 |
| 28618 | dad clarifies this not a food stop | 1 |
28619 rows × 2 columns
time: 14.6 ms (started: 2021-11-21 16:08:56 +00:00)
Checking one sample record
processedDataFrame.loc[102, ['headline','is_sarcastic']]
headline after decades of effort, chemists overseas rep... is_sarcastic 0 Name: 102, dtype: object
time: 5.19 ms (started: 2021-11-21 16:08:56 +00:00)
#Preprocessing the training and test headlines for the word embedding
vocab_size = 30000
embedding_dim = 50
max_length = 100
trunc_type='post'
oov_tok = "<OOV>"
time: 1.66 ms (started: 2021-11-21 16:08:56 +00:00)
Here we are using vocabulary size of 30000 which almost covers almost all the word available in the given dataset. Here we are defining the embedding dimension as 50 and maximum length is 100 for each review.
In the project (Module) requirement PDF, it is mentioned that to get the indices first and then create featues and labels
Task are mentioned as below Get indices for words Create features and labels
If we create indices first, then even the test data will also be seen. And in reality there are fair chances that an unseen word may appear in the document.
So here we shall check the performance of the model on both
1) Applying tokenizer on whole dataset and build the model
2) Applying tokenizer after splitting the dataset into train and test.
tokenizer_whole_dataset = Tokenizer(num_words = vocab_size, oov_token=oov_tok)
tokenizer_whole_dataset.fit_on_texts(processedDataFrame['headline'])
word_index_whole_dataset = tokenizer_whole_dataset.word_index
df_headlines_sequences = tokenizer_whole_dataset.texts_to_sequences(processedDataFrame['headline'])
df_headlines_padded = pad_sequences(df_headlines_sequences,maxlen=max_length, truncating=trunc_type)
time: 1 s (started: 2021-11-21 16:08:56 +00:00)
print(processedDataFrame.iloc[0]['headline'])
print(df_headlines_padded[0])
thirtysomething scientists unveil doomsday clock of hair loss
[ 0 0 0 0 0 0 0 0 0 0 0 0
0 0 0 0 0 0 0 0 0 0 0 0
0 0 0 0 0 0 0 0 0 0 0 0
0 0 0 0 0 0 0 0 0 0 0 0
0 0 0 0 0 0 0 0 0 0 0 0
0 0 0 0 0 0 0 0 0 0 0 0
0 0 0 0 0 0 0 0 0 0 0 0
0 0 0 0 0 0 0 0 16004 355 3167 7474
2644 3 661 1119]
time: 2.02 ms (started: 2021-11-21 16:08:58 +00:00)
print(processedDataFrame.iloc[2]['headline'])
print(df_headlines_padded[2])
eat your veggies: 9 deliciously different recipes
[ 0 0 0 0 0 0 0 0 0 0 0 0
0 0 0 0 0 0 0 0 0 0 0 0
0 0 0 0 0 0 0 0 0 0 0 0
0 0 0 0 0 0 0 0 0 0 0 0
0 0 0 0 0 0 0 0 0 0 0 0
0 0 0 0 0 0 0 0 0 0 0 0
0 0 0 0 0 0 0 0 0 0 0 0
0 0 0 0 0 0 0 0 0 863 33 11427
262 16005 647 1484]
time: 2.16 ms (started: 2021-11-21 16:08:58 +00:00)
train_whole_dataset, validation_whole_dataset = train_test_split(df_headlines_padded, test_size=0.2, random_state=42)
print("Train data shape ::", train_whole_dataset.shape)
print("Validation data shape ::", validation_whole_dataset.shape)
Train data shape :: (22895, 100) Validation data shape :: (5724, 100) time: 10 ms (started: 2021-11-21 16:08:58 +00:00)
#Checking basic statistic about the tokenizer
print(tokenizer_whole_dataset.word_counts)
print(tokenizer_whole_dataset.document_count)
print(tokenizer_whole_dataset.word_index)
print(tokenizer_whole_dataset.word_docs)
OrderedDict([('thirtysomething', 1), ('scientists', 103), ('unveil', 13), ('doomsday', 4), ('clock', 16), ('of', 6292), ('hair', 61), ('loss', 39), ('dem', 4), ('rep', 25), ('totally', 54), ('nails', 13), ('why', 509), ('congress', 139), ('is', 1711), ('falling', 24), ('short', 41), ('on', 2656), ('gender', 29), ('racial', 20), ('equality', 24), ('eat', 48), ('your', 667), ('veggies', 2), ('9', 131), ('deliciously', 1), ('different', 62), ('recipes', 30), ('inclement', 2), ('weather', 28), ('prevents', 5), ('liar', 6), ('from', 1329), ('getting', 211), ('to', 9096), ('work', 203), ('mother', 81), ('comes', 78), ('pretty', 111), ('close', 45), ('using', 69), ('word', 71), ("'streaming'", 1), ('correctly', 5), ('my', 315), ('white', 366), ('inheritance', 2), ('5', 315), ('ways', 124), ('file', 9), ('taxes', 17), ('with', 1956), ('less', 69), ('stress', 33), ('richard', 24), ("branson's", 1), ('global', 53), ('warming', 7), ('donation', 13), ('nearly', 39), ('as', 877), ('much', 149), ('cost', 31), ('failed', 31), ('balloon', 12), ('trips', 9), ('shadow', 14), ('government', 121), ('too', 214), ('large', 26), ('meet', 87), ('in', 4586), ('marriott', 1), ('conference', 28), ('room', 103), ('b', 33), ('lots', 15), ('parents', 194), ('know', 245), ('this', 956), ('scenario', 4), ('lesbian', 16), ('considered', 12), ('a', 3170), ('father', 87), ('indiana', 11), ('and', 1999), ('an', 380), ('amazing', 42), ('one', 566), ('at', 1334), ('that', 874), ('amanda', 4), ('peet', 2), ('told', 62), ('her', 424), ('daughter', 73), ('sex', 164), ("'a", 33), ('special', 64), ("hug'", 1), ('what', 660), ('regarding', 2), ('current', 26), ('treatments', 2), ('for', 3639), ('ebola', 41), ('chris', 69), ('christie', 28), ('suggests', 44), ('hillary', 205), ('clinton', 312), ('was', 349), ('blame', 25), ('boko', 3), ("haram's", 1), ('kidnapping', 4), ('hundreds', 38), ('schoolgirls', 2), ('ford', 29), ('develops', 9), ('new', 1658), ('suv', 5), ('runs', 29), ('purely', 2), ('gasoline', 3), ('uber', 34), ('ceo', 97), ('travis', 4), ('kalanick', 3), ('stepping', 7), ('down', 336), ('trump', 1412), ('economic', 26), ('advisory', 4), ('council', 20), ('area', 500), ('boy', 86), ('enters', 38), ('jumping', 6), ('touching', 20), ('tops', 15), ('doorways', 1), ('phase', 12), ('man', 1379), ('does', 111), ('most', 267), ('his', 678), ('traveling', 19), ('by', 1046), ('gurney', 2), ('leave', 73), ('no', 425), ('person', 90), ('disabilities', 12), ('behind', 107), ('lin', 12), ('manuel', 11), ('miranda', 14), ('would', 274), ('like', 448), ('remind', 17), ('you', 1009), ('put', 89), ('phone', 79), ('away', 132), ('60', 34), ('journalists', 26), ('killed', 94), ('2014', 47), ('targeting', 9), ('international', 30), ('press', 51), ('rises', 14), ('guard', 26), ('video', 230), ('game', 131), ('under', 144), ('strict', 7), ('orders', 36), ('repeatedly', 21), ('pace', 12), ('same', 115), ('stretch', 3), ('hallway', 1), ('how', 849), ('live', 138), ('be', 922), ('110', 1), ('cat', 71), ('so', 254), ('scared', 9), ('shelter', 17), ("won't", 134), ('even', 171), ('look', 176), ('bill', 257), ('shoots', 20), ('republicans', 99), ("'i", 79), ('strongly', 10), ("supported'", 1), ('obamacare', 77), ('secret', 96), ('service', 81), ('agent', 31), ('not', 789), ('about', 1130), ('being', 261), ('david', 61), ('alan', 14), ('grier', 1), ('fan', 72), ('orange', 13), ('era', 32), ('the', 5313), ('growing', 32), ('divide', 7), ('things', 212), ('learned', 54), ('first', 467), ('month', 83), ('having', 109), ('baby', 165), ('lamelo', 1), ('ball', 30), ('scores', 15), ('92', 7), ('points', 37), ('single', 121), ('high', 196), ('school', 263), ('basketball', 17), ("i'm", 71), ('bi', 4), ('it', 857), ('took', 31), ('me', 125), ('21', 21), ('years', 259), ('come', 109), ('out', 930), ('closet', 15), ('say', 222), ('10', 238), ('essential', 9), ('life', 394), ('lessons', 55), ('grandma', 42), ('teenage', 17), ('gunfight', 1), ('isis', 73), ('older', 42), ('but', 244), ('still', 369), ('young', 77), ('heart', 79), ('leading', 43), ('probability', 1), ('researchers', 37), ('confounded', 3), ('three', 83), ('coworkers', 24), ('wearing', 76), ('shirt', 41), ('color', 38), ('day', 507), ('york', 124), ('introduces', 121), ('shoe', 10), ('sharing', 14), ('program', 48), ("city's", 7), ('pedestrians', 3), ('beyoncé', 17), ('sculpted', 1), ('cheese', 29), ('strangely', 2), ('alluring', 1), ('expansive', 3), ('obama', 367), ('state', 235), ('union', 59), ('speech', 108), ('touch', 20), ('patent', 6), ('law', 126), ('entomology', 1), ('films', 21), ('robert', 51), ('altman', 3), ('stars', 59), ('gray', 9), ('prove', 24), ("isn't", 62), ('all', 648), ('bad', 125), ('police', 243), ('woman', 493), ('made', 157), ('up', 925), ('story', 117), ('attack', 120), ('two', 157), ('men', 122), ('hat', 15), ('update', 29), ('naacp', 9), ('demands', 49), ('minority', 15), ('representation', 5), ('upn', 1), ('best', 247), ('clothes', 26), ('child', 187), ('florist', 2), ('who', 646), ('turned', 23), ('gay', 172), ('couple', 140), ('wants', 150), ('supreme', 123), ('court', 188), ('hear', 47), ('case', 106), ("we're", 41), ('processing', 3), ('ending', 19), ('walking', 44), ('dead', 180), ('strangers', 16), ('google', 44), ('project', 29), ('delivers', 26), ('critical', 11), ('info', 7), ("refugees'", 2), ('smartphones', 2), ('history', 122), ('textbook', 6), ('makes', 188), ('hatred', 7), ('alive', 17), ('students', 122), ('ridiculously', 5), ('successful', 20), ('people', 392), ('think', 118), ('differently', 6), ('report', 604), ('bridge', 14), ('probably', 77), ('has', 620), ('whole', 62), ('mess', 18), ('bats', 7), ('there', 144), ('texas', 77), ('urge', 21), ('style', 47), ('immigration', 60), ('crackdown', 14), ('tom', 55), ('hanks', 9), ('brags', 4), ("'smokin'", 1), ("hot'", 2), ('rita', 2), ('wilson', 17), ('after', 1013), ('29', 7), ('marriage', 95), ('god', 135), ('strong', 20), ('bring', 57), ('back', 355), ('dinosaurs', 5), ('lgbt', 50), ('christians', 7), ('speak', 37), ('love', 204), ('sinner', 2), ('hate', 60), ('sin', 4), ('cut', 52), ('anymore', 30), ('handshake', 1), ('unusually', 2), ('angle', 2), ('velocity', 1), ('mom', 212), ('keeps', 53), ('sending', 20), ('newspaper', 23), ('clippings', 1), ('former', 111), ('classmates', 9), ('have', 554), ('been', 150), ('murdered', 11), ('make', 351), ('stop', 177), ('coed', 1), ('rec', 2), ('softball', 4), ('team', 94), ('trouble', 19), ('finding', 46), ('enough', 130), ('hyper', 1), ('competitive', 5), ('ruin', 15), ('experience', 53), ('brutalist', 1), ('beaver', 2), ('constructs', 3), ('paul', 127), ('rudolph', 1), ('inspired', 36), ('dam', 5), ('jindal', 3), ('westboro', 2), ('baptist', 3), ('members', 44), ('protest', 60), ('funerals', 2), ('face', 133), ('arrest', 18), ('gop', 262), ('congressman', 65), ('may', 210), ('vote', 96), ('president', 228), ('donald', 473), ("trump's", 403), ('latest', 67), ('attempt', 39), ('repeal', 38), ('seth', 19), ('meyers', 14), ('sick', 55), ('suicide', 46), ('bombing', 21), ('can', 489), ('spot', 47), ('warning', 35), ('signs', 54), ('department', 92), ('warns', 87), ('americans', 288), ('abroad', 11), ('avoid', 45), ('lame', 8), ('amsterdam', 4), ('windmill', 1), ('tour', 46), ('meatless', 3), ('monday', 28), ('portuguese', 2), ('mashup', 4), ('now', 394), ('message', 58), ('facebook', 130), ('families', 42), ('missing', 83), ('flight', 41), ('passengers', 28), ('just', 667), ('hoping', 54), ('media', 128), ('gets', 202), ('closure', 5), ('needs', 82), ('fender', 2), ('releases', 114), ('hybrid', 3), ('gas', 36), ('electric', 16), ('guitar', 12), ('romney', 68), ('volunteers', 6), ('going', 238), ('door', 48), ('let', 85), ('supporters', 47), ("president's", 24), ('friday', 33), ('talking', 69), ('prelude', 1), ('silly', 6), ('season', 131), ('sparkle', 1), ('2', 211), ('year', 541), ('old', 417), ("who's", 36), ('next', 173), ('crush', 9), ("hollywood's", 6), ('biggest', 46), ('endure', 4), ('long', 150), ('lines', 22), ('oscars', 49), ('security', 102), ('screening', 6), ('congresswoman', 6), ('fights', 13), ('gun', 144), ('control', 94), ('because', 95), ('she', 244), ('almost', 52), ('lost', 75), ('violence', 95), ('raise', 37), ('kids', 192), ("'love", 10), ("loved'", 2), ('want', 174), ('meetings', 8), ('more', 585), ('productive', 8), ('start', 79), ('elizabeth', 45), ("warren's", 4), ('pick', 43), ('wins', 71), ("ohio's", 3), ('democratic', 69), ('gubernatorial', 5), ('primary', 37), ('biting', 7), ('argument', 20), ('over', 536), ('ear', 7), ('longtime', 15), ('reader', 10), ('lib', 3), ('slaves', 6), ('mainstream', 10), ('bias', 9), ('sites', 14), ('wideawakepatriot', 1), ('com', 23), ('beijing', 8), ('fire', 116), ('extinguishes', 1), ('massive', 58), ('five', 47), ('alarm', 11), ('burning', 24), ('cloud', 6), ('smog', 4), ('china', 59), ('intensifies', 2), ('pressure', 18), ('north', 137), ('korea', 74), ('defiant', 14), ('sanders', 113), ('camp', 28), ("ain't", 6), ('when', 316), ('sheep', 1), ('are', 674), ('watching', 74), ('mind', 51), ('toronto', 6), ('film', 110), ('festival', 31), ('jake', 14), ("gyllenhaal's", 1), ("'demolition", 1), ("'", 386), ('stunning', 35), ("'lobster'", 1), ('super', 88), ('pac', 16), ('political', 88), ('consultants', 1), ('oilman', 1), ('wild', 36), ('ride', 36), ("'active", 1), ('shooter', 22), ('reports', 86), ('endless', 7), ('background', 14), ('hum', 2), ('modern', 29), ('american', 248), ('axelrod', 2), ('will', 607), ('seen', 53), ('complex', 12), ('alternative', 14), ('sleater', 1), ('kinney', 2), ("bowie's", 2), ("'rebel", 1), ("rebel'", 1), ('anthem', 18), ('2017', 46), ('kirsten', 6), ('gillibrand', 6), ('only', 272), ('regrets', 17), ('calling', 31), ('al', 60), ('franken', 12), ('quit', 18), ('sooner', 4), ('wells', 8), ('fargo', 7), ('sued', 9), ('barring', 1), ('daca', 11), ('recipients', 7), ('student', 97), ('loans', 9), ('historical', 39), ('archives', 33), ('publick', 1), ('spectacle', 2), ('israel', 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('groundbreaking', 5), ('study', 277), ('finds', 252), ('gratification', 4), ('deliberately', 2), ('postponed', 3), ('nick', 17), ('cannon', 8), ('responds', 40), ('mariah', 9), ("carey's", 3), ('engagement', 18), ('way', 296), ('midlife', 3), ('obesity', 11), ('speed', 25), ("alzheimer's", 16), ('70', 22), ('endorsements', 4), ('staring', 13), ('bedroom', 15), ('ceiling', 16), ('4', 171), ('hours', 79), ('diets', 1), ('poor', 45), ('improving', 7), ('decades', 28), ('effort', 35), ('chemists', 1), ('overseas', 5), ("'nano'", 1), ('breakthrough', 10), ('produce', 9), ('section', 24), ('bursts', 9), ('into', 589), ('laughter', 6), ('ferrell', 4), ('casual', 12), ('remark', 8), ('apples', 2), ('dad', 152), ('hands', 57), ('worst', 50), ('audition', 5), ('ever', 152), ('or', 230), ('danger', 6), ('playing', 52), ('paddle', 4), ('chewing', 6), ('gum', 8), ('singing', 15), ('we', 283), ('built', 14), ('simultaneously', 6), ('states', 82), ('help', 150), ('million', 161), ('parent', 31), 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('foley', 2), ('documentary', 40), ('making', 127), ('movie', 123), ('childhood', 17), ('pal', 5), ('truth', 37), ('40', 58), ("'sleeping", 1), ("it'", 9), ('really', 251), ('solve', 16), ('problems', 42), ('annoyed', 21), ('movers', 2), ("weren't", 8), ('expecting', 14), ('client', 6), ('belongings', 1), ('toeing', 1), ('race', 86), ('line', 113), ('i', 316), ('am', 39), ('essay', 15), ("'sexual", 1), ("paranoia'", 1), ('caused', 15), ('frenzy', 3), ('northwestern', 1), ('university', 49), ('argentina', 7), ('tightens', 4), ('anticipation', 3), ('numerous', 5), ('criminals', 9), ('arriving', 8), ('g20', 4), ('senior', 27), ('citizen', 11), ('shaken', 9), ('diminished', 1), ('bawdy', 1), ('limerick', 1), ('recall', 18), ('filled', 22), ('gratitude', 10), ('sight', 18), ('other', 183), ('customer', 32), ('nice', 39), ('restaurant', 73), ('jeans', 8), ('disastrous', 7), ('swimsuit', 10), ('space', 50), ('saving', 24), ('ideas', 44), ('small', 74), ('kitchens', 1), ("'stranger", 11), 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28619
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'qualms': 17894, 'caging': 17895, "'aryan": 17896, "notions'": 17897, "byrne's": 17898, 'uninhibited': 17899, 'rescuing': 17900, "santa'": 17901, 'disburses': 17902, 'podcaster': 17903, 'circumstantial': 17904, "evidence'": 17905, 'activated': 17906, "'pigs": 17907, "blanket'": 17908, 'overpass': 17909, "teens'": 17910, 'vax': 17911, 'equinox': 17912, 'fundamentals': 17913, 'amazonian': 17914, 'bulldozer': 17915, 'treads': 17916, 'brandishes': 17917, 'popeye': 17918, "'dese": 17919, 'bombinks': 17920, 'disgustipating': 17921, 'vacate': 17922, 'pastoral': 17923, 'frustrating': 17924, 'cruces': 17925, 'impale': 17926, '“drain': 17927, 'swamp”': 17928, 'dilute': 17929, 'scientifically': 17930, 'sighted': 17931, 'challengers': 17932, 'purported': 17933, 'kaia': 17934, 'brawls': 17935, 'undetectable': 17936, 'transmit': 17937, "doin'": 17938, 'maasai': 17939, "bbc's": 17940, 'looseyia': 17941, 'farmworker': 17942, 'cone': 17943, 'sail': 17944, 'antarctica': 17945, "warcraft'": 17946, 'leeching': 17947, 'batali': 17948, 'unhrc': 17949, 'yearlong': 17950, 'muse': 17951, 'mucus': 17952, 'puddles': 17953, 'ulcers': 17954, 'wrest': 17955, 'portraiture': 17956, 'trappings': 17957, "'pti'": 17958, 'lager': 17959, 'tavern': 17960, "five'": 17961, 'herman': 17962, 'helmed': 17963, "'freeze": 17964, "frame'": 17965, 'cornnuts': 17966, 'stammers': 17967, "'nutsarito'": 17968, "'juke": 17969, "monk's": 17970, 'bowed': 17971, 'earmark': 17972, '113': 17973, 'identifies': 17974, "rover's": 17975, 'horseconnect': 17976, 'ionosphere': 17977, 'exploding': 17978, 'infesting': 17979, 'sarandon': 17980, 'tattletale': 17981, 'mpaa': 17982, "'regular'": 17983, 'caffeine': 17984, "'paris": 17985, 'ghostwriter': 17986, "reiser's": 17987, 'burkinis': 17988, "midwife's": 17989, "promise'": 17990, 'moderates': 17991, "gettin'": 17992, "'des": 17993, 'moines': 17994, 'des': 17995, "perate'": 17996, 'arquette': 17997, "'fought'": 17998, 'ernie': 17999, "cc'd": 18000, 'dixieland': 18001, 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'vestigial': 18055, 'comforted': 18056, 'jolson': 18057, "'witches'": 18058, "marks'": 18059, 'experiments': 18060, 'punt': 18061, 'pronged': 18062, 'overlook': 18063, "newark's": 18064, "instagrammer's": 18065, 'willams': 18066, 'festive': 18067, "'failing": 18068, 'habsburg': 18069, "disick's": 18070, "carlson's": 18071, 'duca': 18072, 'arse': 18073, 'tiara': 18074, 'workbench': 18075, "'nonstarter": 18076, 'frustrate': 18077, 'bootleg': 18078, 'philosopher': 18079, 'juicero': 18080, 'juicer': 18081, 'shredyourex': 18082, 'dishware': 18083, "'country": 18084, "x'": 18085, 'greenwald': 18086, 'multipacks': 18087, 'scaring': 18088, 'iffy': 18089, 'nonchalant': 18090, 'faulty': 18091, 'dysfunction': 18092, 'parkway': 18093, 'waukegan': 18094, 'cafepress': 18095, 'improperly': 18096, 'librarian': 18097, 'amassed': 18098, 'humbly': 18099, 'randos': 18100, 'cylindrical': 18101, 'techview': 18102, 'linus': 18103, 'torvalds': 18104, 'linux': 18105, 'sidebar': 18106, 'demonstrated': 18107, "'stability'": 18108, "'competence'": 18109, "d23's": 18110, 'sanderson': 18111, "suspect's": 18112, 'calle': 18113, "'parent": 18114, 'completing': 18115, 'sparked': 18116, 'duration': 18117, "semitic'": 18118, 'bankrolling': 18119, '135': 18120, 'modernity': 18121, 'moroccan': 18122, 'nspw2017': 18123, "engineer's": 18124, 'sclerosis': 18125, "'duke": 18126, "burgundy'": 18127, 'pantone': 18128, 'starstruck': 18129, '1106': 18130, 'smattering': 18131, 'stewman': 18132, "quest's": 18133, 'phife': 18134, 'dawg': 18135, 'debunking': 18136, 'jung': 18137, 'thunderstorm': 18138, 'legoland': 18139, "'swamp": 18140, "cabinet'": 18141, "'wants": 18142, 'ritter': 18143, "'storm'": 18144, 'whirlwind': 18145, 'wrecked': 18146, 'weakling': 18147, 'bless': 18148, 'delarge': 18149, 'droogs': 18150, 'amidst': 18151, 'extremism': 18152, "'friday": 18153, "lights'": 18154, "same'": 18155, 'skiers': 18156, "'accountability'": 18157, 'speechwriting': 18158, 'entomologists': 18159, 'retract': 18160, 'clump': 18161, 'osteoporosis': 18162, 'ünited': 18163, 'stätes': 18164, 'toughens': 18165, 'umlauts': 18166, 'cokes': 18167, 'sens': 18168, "wound'": 18169, "decade'": 18170, "'acceptable'": 18171, 'oddsmakers': 18172, 'moody': 18173, "'demolish": 18174, "ceiling'": 18175, 'lovell': 18176, "'finish": 18177, 'portia': 18178, 'frieze': 18179, 'functionless': 18180, 'translucent': 18181, 'confederacy': 18182, "columbia'": 18183, "'pitzer's": 18184, 'alleviate': 18185, 'transplanted': 18186, 'collaborating': 18187, 'judi': 18188, 'dench': 18189, "spoon's": 18190, 'pint': 18191, 'jarring': 18192, 'hinky': 18193, 'intimidating': 18194, 'fka': 18195, 'twigs': 18196, 'marmont': 18197, 'wades': 18198, 'sleekest': 18199, 'catcalls': 18200, 'bathe': 18201, "pluto's": 18202, "'heart'": 18203, 'derby': 18204, 'overboard': 18205, 'lourea': 18206, 'presented': 18207, 'macrowave': 18208, 'defrost': 18209, "'dallas'": 18210, 'portends': 18211, 'koko': 18212, 'dieselgate': 18213, 'gunshots': 18214, 'pullups': 18215, 'oxfam': 18216, 'epidemics': 18217, "strange'": 18218, 'showbiz': 18219, "'has": 18220, 'compendium': 18221, '812': 18222, '683': 18223, 'norad': 18224, 'femstat': 18225, 'rossi': 18226, 'spumanti': 18227, "'fluidity'": 18228, 'kacy': 18229, 'rapaport': 18230, 'deavere': 18231, '44th': 18232, 'peshawar': 18233, 'absent': 18234, 'recommending': 18235, 'odyssey': 18236, "cleaner's": 18237, 'garment': 18238, 'josé': 18239, 'andrés': 18240, 'tempting': 18241, 'maharishi': 18242, 'rupee': 18243, 'startled': 18244, "'beware": 18245, "hubris'": 18246, 'filmmaking': 18247, 'superficiality': 18248, "rome's": 18249, "'heartbeat": 18250, 'buchanan': 18251, 'radicalizing': 18252, 'errands': 18253, 'sudoku': 18254, '8x10': 18255, 'danza': 18256, 'staging': 18257, "'defrosted": 18258, "'therapy'": 18259, "'contusions'": 18260, 'althea': 18261, 'domination': 18262, 'marv': 18263, 'babylon': 18264, '539': 18265, 'architecture': 18266, 'contributed': 18267, 'stormtroopers': 18268, "lord'": 18269, "cowell's": 18270, 'incumbency': 18271, "'maternal": 18272, "instincts'": 18273, 'selecting': 18274, 'samaritan': 18275, 'unpredictable': 18276, 'dirtydenier': 18277, 'tasks': 18278, "'pro": 18279, "israel'": 18280, "'bridge": 18281, "ahead'": 18282, 'noaa': 18283, 'introspect': 18284, 'assembling': 18285, 'pointy': 18286, 'antismoking': 18287, 'chromat': 18288, 'worships': 18289, 'twos': 18290, 'bolted': 18291, 'carved': 18292, 'docents': 18293, 'fuse': 18294, 'interacting': 18295, 'libertarians': 18296, 'biochemical': 18297, 'stressful': 18298, 'granger': 18299, 'loch': 18300, '4se': 18301, "opponents'": 18302, 'terrors': 18303, 'metallurgists': 18304, "'polonium": 18305, "plus'": 18306, 'pushy': 18307, 'hermit': 18308, 'rewriting': 18309, 'sparkling': 18310, '\u200bexplains\u200b': 18311, 'pansexual': 18312, 'moonwalks': 18313, 'samuel': 18314, "'boston": 18315, "sucks'": 18316, 'pilsner': 18317, '49ers': 18318, 'ot': 18319, 'articulate': 18320, 'intensifying': 18321, "buttercup'": 18322, 'backdrop': 18323, 'mitty': 18324, 'uncashed': 18325, 'quadriscuits': 18326, 'swarms': 18327, 'frosty': 18328, 'doorknob': 18329, 'mcenroe': 18330, 'nea': 18331, 'claes': 18332, "oldenburg's": 18333, "'giant": 18334, 'perk': 18335, 'enjoyable': 18336, "'such": 18337, "forecaster's": 18338, 'spooks': 18339, "nba's": 18340, 'remastered': 18341, "'shybot'": 18342, 'roams': 18343, 'handy': 18344, 'knifepoint': 18345, 'alvah': 18346, 'roebuck': 18347, "sears'": 18348, 'amaranth': 18349, 'hick': 18350, 'nobodies': 18351, 'deduced': 18352, 'prefaces': 18353, "test's": 18354, 'narcan': 18355, 'biplanes': 18356, 'fumigate': 18357, 'falters': 18358, 'inked': 18359, 'masterful': 18360, "'and'": 18361, '23andme': 18362, 'klugman': 18363, 'tania': 18364, 'bruguera': 18365, "rights'": 18366, 'thinnest': 18367, 'toasting': 18368, 'ketogenic': 18369, "'scared": 18370, "straight'": 18371, 'fineo': 18372, 'stalked': 18373, "'sh": 18374, 'overshoots': 18375, 'devout': 18376, "another's": 18377, 'quilting': 18378, 'csa': 18379, 'gchats': 18380, 'marcus': 18381, 'mariota': 18382, '43rd': 18383, 'patrols': 18384, 'crowdfund': 18385, 'gineering': 18386, 'integrate': 18387, 'gloved': 18388, "'catfishing'": 18389, 'spurred': 18390, 'incidence': 18391, 'beams': 18392, "'stargate": 18393, 'sg': 18394, 'machado': 18395, "candidate'": 18396, 'polk': 18397, 'spire': 18398, "gym's": 18399, 'sympathize': 18400, 'pettiness': 18401, 'sacked': 18402, 'deception': 18403, 'medallist': 18404, 'violation': 18405, "consequences'": 18406, 'enabling': 18407, 'duckling': 18408, 'colt': 18409, 'skews': 18410, "asia's": 18411, "'sole": 18412, "survivor'": 18413, "roots'": 18414, "'tomorrow'": 18415, 'umpqua': 18416, "'gun": 18417, "samantha's": 18418, "scott's": 18419, 'misdirection': 18420, 'incivility': 18421, 'headfirst': 18422, "'jungle": 18423, 'nawal': 18424, 'teleworks': 18425, 'rescheduling': 18426, 'gregorios': 18427, 'yohanna': 18428, 'ibrahim': 18429, 'boulos': 18430, 'yazigi': 18431, "right's": 18432, 'tribeca': 18433, 'cartels': 18434, 'sweeney': 18435, 'lydia': 18436, 'polgreen': 18437, 'hagar': 18438, 'bog': 18439, 'eternity': 18440, 'udpate': 18441, 'cheadle': 18442, 'inconveniencing': 18443, 'owed': 18444, 'mathis': 18445, 'mancini': 18446, 'anson': 18447, 'rmh': 18448, 'relocate': 18449, 'roadblock': 18450, 'demitri': 18451, "anchorwoman's": 18452, 'ethnicity': 18453, 'theoretically': 18454, 'hideo': 18455, 'kojima': 18456, 'cutscene': 18457, '1983': 18458, 'speedboat': 18459, "blair's": 18460, "'outburst'": 18461, "heston's": 18462, 'apparel': 18463, 'redid': 18464, 'steaks™': 18465, "constituent's": 18466, 'rotates': 18467, 'demonically': 18468, 'underdraft': 18469, 'mentoring': 18470, 'neapolitan': 18471, 'lady—remember': 18472, '—as': 18473, 'thermometer': 18474, 'reeva': 18475, 'steenkamp': 18476, "'rest": 18477, 'directtv': 18478, 'cloney': 18479, 'pheromone': 18480, 'secretion': 18481, "'introduction": 18482, "algebra'": 18483, "fox'": 18484, 'jj': 18485, 'births': 18486, 'ragsdale': 18487, 'homan': 18488, 'deepwater': 18489, 'cicilline': 18490, "equality'": 18491, 'polka': 18492, 'loeffelmacher': 18493, "ronaldo's": 18494, 'disavowing': 18495, 'nachos': 18496, 'escalate': 18497, 'fashionistas': 18498, 'firefox': 18499, 'sociocultural': 18500, "seriously'": 18501, 'entendre': 18502, 'stardew': 18503, 'bankrupted': 18504, "'tbfoodsllc'": 18505, "fury'": 18506, 'philipps': 18507, "ledger's": 18508, 'psychics': 18509, "krieger's": 18510, 'setbacks': 18511, 'dumptruck': 18512, "'homeland": 18513, "program'": 18514, '1933': 18515, "'cavalcade'": 18516, "'lady": 18517, 'salvadoran': 18518, 'semite': 18519, 'woodstock': 18520, 'kingpins': 18521, 'pantomimed': 18522, 'lasso': 18523, 'outsources': 18524, 'hominid': 18525, 'unambitious': 18526, "negro'": 18527, 'myers': 18528, "rogers'": 18529, 'servicepeople': 18530, "introvert's": 18531, 'cubicle': 18532, 'recidivism': 18533, "'stars": 18534, 'nelly': 18535, 'cited': 18536, 'breezy': 18537, 'poe': 18538, "'freeze'": 18539, 'acne': 18540, 'dizziness': 18541, 'nausea': 18542, 'clotting': 18543, 'loews': 18544, 'versace': 18545, "code'": 18546, 'tricking': 18547, 'ceremonies': 18548, 'dax': 18549, "bud'": 18550, 'nuestra': 18551, 'palabra': 18552, 'knot': 18553, 'untangling': 18554, 'midas': 18555, 'scowls': 18556, 'maduro': 18557, 'methodist': 18558, 'wamu': 18559, 'chaplev': 18560, "passengers'": 18561, "'neo": 18562, "nazi'": 18563, 'riots': 18564, 'mildfires': 18565, 'amble': 18566, 'rossini': 18567, 'caramoor': 18568, 'rosé': 18569, 'holtzclaw': 18570, "'lopsided": 18571, "account'": 18572, 'bronchitis': 18573, 'potties': 18574, 'stinky': 18575, "fair's": 18576, '300th': 18577, 'arranging': 18578, "grandchildren's": 18579, "'dishonest'": 18580, 'zach': 18581, 'braff': 18582, 'milano': 18583, 'pivoting': 18584, 'floundering': 18585, "'russiagate'": 18586, 'remover': 18587, 'unsliced': 18588, 'speakeasy': 18589, 'embalm': 18590, 'laboratory': 18591, 'crichton': 18592, 'operates': 18593, 'northam': 18594, 'wizards': 18595, 'pellets': 18596, 'firstborn': 18597, 'apprehends': 18598, "'bet": 18599, "honors'": 18600, "'sing": 18601, "'annihilate'": 18602, 'duller': 18603, 'starred': 18604, 'sri': 18605, 'srinivasan': 18606, 'rossdale': 18607, "painful'": 18608, "mara's": 18609, 'angelic': 18610, 'panetta': 18611, 'supervillain': 18612, 'sherman': 18613, 'alexie': 18614, "'noise": 18615, "headphones'": 18616, "'unfairly'": 18617, 'compatriots': 18618, 'shortz': 18619, "solomon's": 18620, 'awestruck': 18621, 'shielded': 18622, 'quagmire': 18623, 'escalator': 18624, 'rethinking': 18625, 'loyalties': 18626, 'airtime': 18627, "belly'": 18628, "'shitfaced": 18629, "drunk'": 18630, 'olean': 18631, 'stoneheart': 18632, 'antifa': 18633, 'supplying': 18634, 'oldies': 18635, 'goodies': 18636, 'champs': 18637, 'triumphal': 18638, 'pranked': 18639, 'muhammed': 18640, 'ergonomic': 18641, 'lumbar': 18642, "limbaugh's": 18643, 'larson': 18644, "marvel'": 18645, 'workouts': 18646, 'chides': 18647, 'touting': 18648, 'labute': 18649, 'frances': 18650, 'cobain': 18651, 'prehab': 18652, 'optimists': 18653, 'snuggle': 18654, 'perks': 18655, 'associate': 18656, "chapo's": 18657, "musician's": 18658, 'buckets': 18659, "rumsfeld's": 18660, 'barracks': 18661, 'evans': 18662, 'dru': 18663, "'field": 18664, "dreams'": 18665, 'frames': 18666, 'reinhart': 18667, 'insensitive': 18668, 'tanned': 18669, 'exquisitely': 18670, 'coiffed': 18671, 'urinary': 18672, 'noel': 18673, "comrie's": 18674, 'volley': 18675, "'children's": 18676, 'niall': 18677, 'horan': 18678, "ginuwine's": 18679, "'pony'": 18680, 'vindicated': 18681, "deodorant's": 18682, 'alternativefacts': 18683, 'sanford': 18684, "laws'": 18685, 'jade': 18686, 'pettyjohn': 18687, "nickelodeon's": 18688, 'katee': 18689, 'sackhoff': 18690, 'hy': 18691, 'genic': 18692, 'apportionment': 18693, 'gladness': 18694, 'participation': 18695, 'comfier': 18696, 'evacuation': 18697, "'wondersplint'": 18698, 'fractures': 18699, 'fuller': 18700, 'kondo': 18701, 'untidy': 18702, 'wearhouse': 18703, 'trousers': 18704, 'topher': 18705, "grace's": 18706, "chang's": 18707, "lively's": 18708, 'hyperbole': 18709, 'uncovering': 18710, 'kickline': 18711, 'squealing': 18712, 'jawa': 18713, 'dusted': 18714, 'sf': 18715, 'tomm': 18716, 'lasorda': 18717, 'motivates': 18718, 'streaking': 18719, 'kidz': 18720, 'bop': 18721, "'meow": 18722, "nisota'": 18723, 'piazza': 18724, "'cavalleria": 18725, "rusticana'": 18726, "'pagliacci'": 18727, 'tumbleweed': 18728, 'pubes': 18729, 'desolate': 18730, 'ob': 18731, 'gyn': 18732, 'template': 18733, 'reinvented': 18734, 'platt': 18735, 'competencies': 18736, 'headband': 18737, 'ameringer': 18738, 'mcenery': 18739, 'yohe': 18740, 'receding': 18741, "area's": 18742, 'bandai': 18743, "kindergartener's": 18744, 'incomprehensible': 18745, 'infiltrated': 18746, 'automakers': 18747, 'taxed': 18748, 'modeled': 18749, "'propaganda'": 18750, 'mund': 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19555, 'headdress': 19556, 'fraudsters': 19557, 'reap': 19558, 'lipped': 19559, "race's": 19560, 'traditions': 19561, "'cliff": 19562, "clavin'": 19563, 'converts': 19564, 'hokey': 19565, 'triggering': 19566, 'buzzed': 19567, 'digitization': 19568, 'impotently': 19569, 'impassable': 19570, 'orozco': 19571, 'qualifying': 19572, 'rams': 19573, "strength'": 19574, '2525': 19575, 'entourage': 19576, 'reggie': 19577, "fox's": 19578, 'atheletes': 19579, 'wharton—wharton': 19580, 'airmen': 19581, 'reimpose': 19582, 'chekhovian': 19583, 'americorps': 19584, 'prefaced': 19585, "events'": 19586, "'rig'": 19587, 'carabiner': 19588, 'rappel': 19589, 'jebbush': 19590, 'supersized': 19591, "tff's": 19592, 'orzabal': 19593, "al's": 19594, "fame'": 19595, 'ninetysomethings': 19596, 'photoshops': 19597, "postsecret'": 19598, 'lurks': 19599, 'prostate': 19600, "georgia's": 19601, 'motives': 19602, 'aveage': 19603, 'neill': 19604, 'blomkamp': 19605, "'alien'": 19606, 'scheduled': 19607, "mcgregor's": 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"duvernay's": 29700, "rec'": 29701, 'morales': 29702, 'moonlit': 29703, 'bonfire': 29704, 'conjure': 29705, 'embellished': 29706, 'frenchie': 29707, "soaked'": 29708, 'predisposition': 29709, 'clotheslines': 29710, 'interfered': 29711, 'convent': 29712, 'lacking': 29713, 'brontë–emily': 29714, 'brontë': 29715, 'lingered': 29716, 'pittsburgh': 29717, 'superbug': 29718, 'thousandth': 29719, 'versatile': 29720, 'assembled': 29721, 'minimal': 29722, "ship'": 29723, 'iverson': 29724, 'realest': 29725, 'famer': 29726, "'stab": 29727, 'bitches': 29728, 'naptime': 29729, 'reneged': 29730, "buddha'": 29731, '433': 29732, 'interaction': 29733, 'flusher': 29734, 'snared': 29735, 'patrolmen': 29736, 'wipeout': 29737, "'midlife": 29738, 'hachette': 29739, 'smartness': 29740, 'alcorn': 29741, 'shakily': 29742, 'curfews': 29743, 'helium': 29744, 'juno': 29745, 'squeaky': 29746, 'joyous': 29747, 'eid': 29748, 'ugaaso': 29749, 'abukar': 29750, "boocow's": 29751, 'mogadishu': 29752, 'nostalgically': 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30021, 'menswear': 30022, 'imminent': 30023, 'mirjana': 30024, 'lucic': 30025, 'baroni': 30026, 'boast': 30027, "'juno'": 30028, 'transplants': 30029, 'influencers': 30030, 'constitutions': 30031, 'dusk': 30032, 'nights': 30033, 'betrayed': 30034, 'simpleton': 30035, 'outweighed': 30036, 'vivid': 30037, "mosul's": 30038, 'doubters': 30039, 'feasibility': 30040, 'deserts': 30041, 'pressuring': 30042, 'kofi': 30043, 'annan': 30044, 'mumbling': 30045, 'bedsore': 30046, '155th': 30047, 'kristin': 30048, "davis'": 30049, "priest'": 30050, "'rising'": 30051, "acosta's": 30052, 'assertion': 30053, 'achievment': 30054, 'kidnappings': 30055, "we'd": 30056, "'col": 30057, "miranda's": 30058, 'labeouf': 30059, 'walletless': 30060, 'bedpost': 30061, "'growing": 30062, 'torment': 30063, 'slinking': 30064, "'deadpool": 30065, "brolin's": 30066, "'but": 30067, 'gingerbread': 30068, 'sontag': 30069, 'intolerance': 30070, "'bed": 30071, "england'": 30072, "'secret'": 30073, 'sired': 30074, 'barnacle': 30075, 'discharge': 30076, 'ionceoverheard': 30077, 'gladiator': 30078, "'turbo'": 30079, 'bb': 30080, 'flings': 30081, 'forbearance': 30082, 'combatting': 30083, 'disorderly': 30084, 'intoxication': 30085, "'chewbacca": 30086, 'wrecks': 30087, 'cayman': 30088, 'jayne': 30089, 'krentz': 30090, "marry'": 30091, "carrier's": 30092, 'saltless': 30093, 'ulterior': 30094, 'vying': 30095, "'inner": 30096, "monitor'": 30097, 'masterwork': 30098, "leaf'": 30099, 'indictments': 30100, 'eli': 30101, 'millionsmarchsf': 30102, 'voltage': 30103, 'electrified': 30104, 'trumpland': 30105, 'victories': 30106, 'friendshipgoals': 30107, "says'": 30108, "t's": 30109, 'storylines': 30110, 'crete': 30111, "'confession'": 30112, 'cabins': 30113, 'sightings': 30114, 'psylandorian': 30115, 'bookworm': 30116, 'shareholdaz': 30117, "ghostwriters'": 30118, 'buffs': 30119, 'blobs': 30120, 'satyajit': 30121, "ray's": 30122, "holiday's": 30123, 'horsepower': 30124, 'bathtub': 30125, '984': 30126, '828': 30127, 'cutoff': 30128, 'dozing': 30129, 'gladys': 30130, "'engaged'": 30131, 'embed': 30132, 'principle': 30133, "natural'": 30134, "doj's": 30135, 'renewables': 30136, "'gaza'": 30137, "earnest's": 30138, 'wh': 30139, 'bourdain': 30140, '2nd': 30141, 'breathalyzer': 30142, 'leviathan': 30143, 'awoken': 30144, 'sonar': 30145, 'seatbacks': 30146, 'pleases': 30147, 'hentai': 30148, "mingle'": 30149, 'starnes': 30150, "'official": 30151, 'subvert': 30152, 'sketched': 30153, "'pride": 30154, "prejudice'": 30155, "critic's": 30156, 'garnering': 30157, 'urkel': 30158, 'foreheads': 30159, 'rappers': 30160, 'broadcasting': 30161, 'waithe': 30162, 'shriver': 30163, 'fark': 30164, 'lebowski': 30165, 'accompanying': 30166, 'presumes': 30167, 'gulag': 30168, 'siberia': 30169, 'attire': 30170, 'ogre': 30171, "everything's": 30172, 'imaginable': 30173, 'mechanism': 30174, 'frills': 30175, 'pomp': 30176, 'chillingly': 30177, 'reappears': 30178, "money'": 30179, 'begun': 30180, "'tort": 30181, "reform'": 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'dominos': 1, 'manned': 1, 'koran': 1, "q'": 1, "'fry": 1, 'butternut': 1, 'majesty': 1, 'walkers': 1, 'krieger': 1, 'pushier': 1, "gavin's": 1, "'teen'": 1, 'rollerball': 1, 'll': 1, 'slime': 1, 'cocooned': 1, 'ooze': 1, 'excludes': 1, "liberty's": 1, "sinatra's": 1, 'chaka': 1, "'czar'": 1, 'appointing': 1, 'slowing': 1, 'meara': 1, 'everlast': 1, 'maitre': 1, "d'": 1, "ref's": 1, 'blotter': 1, 'clarksburg': 1, 'baftas': 1, 'raiser': 1, 'fanciful': 1, 'rentboy': 1, 'rentboys': 1, "'quality'": 1, "'1989'": 1, "'gremlin": 1, "consumer's": 1, 'rhinoceros': 1, 'birkenstocks': 1, 'surmise': 1, 'heartache': 1, 'tca': 1, 'mulling': 1, 'mercator': 1, 'aches': 1, 'lovelorn': 1, "newswoman's": 1, 'vibrant': 1, "rim'": 1, 'reassuring': 1, "bernanke's": 1, "'pacific": 1, 'grooviest': 1, "remain'": 1, "'renewed": 1, 'spectators': 1, "row'": 1, 'redo': 1, "'entertainment": 1, "'sorority": 1, 'diagnosing': 1, 'bluefish': 1, 'saor': 1, 'venetian': 1, "'inquiry'": 1, 'pokémon': 1, "'polack'": 1, "antonio's": 1, 'rosh': 1, 'hashasha': 1, 'orbiting': 1})
time: 36.1 ms (started: 2021-11-21 16:08:58 +00:00)
As displayed in above cell, the words are having the index based on their occurance and these indexes will be used to generate the embeddings for the words.
print("Vocab Size :: " , len(word_index_whole_dataset))
Vocab Size :: 30885 time: 855 µs (started: 2021-11-21 16:08:58 +00:00)
train, validation = train_test_split(processedDataFrame, test_size=0.2, random_state=42)
train.shape, validation.shape
((22895, 2), (5724, 2))
time: 9.3 ms (started: 2021-11-21 16:08:58 +00:00)
train.reset_index(inplace=True, drop=True)
validation.reset_index(inplace=True, drop=True)
time: 1.12 ms (started: 2021-11-21 16:08:58 +00:00)
X_train = train['headline']
y_train = train['is_sarcastic']
time: 1.21 ms (started: 2021-11-21 16:08:58 +00:00)
X_test = validation['headline']
y_test = validation['is_sarcastic']
time: 1.15 ms (started: 2021-11-21 16:08:58 +00:00)
tokenizer_split_data = Tokenizer(num_words = vocab_size, oov_token=oov_tok)
tokenizer_split_data.fit_on_texts(X_train)
word_index_split_data = tokenizer_split_data.word_index
train_sequences_split_data = tokenizer_split_data.texts_to_sequences(X_train)
train_padded_split_data = pad_sequences(train_sequences_split_data,maxlen=max_length, truncating=trunc_type)
test_sequences_split_data = tokenizer_split_data.texts_to_sequences(X_test)
test_padded_split_data = pad_sequences(test_sequences_split_data,maxlen=max_length, truncating=trunc_type)
time: 1.01 s (started: 2021-11-21 16:08:58 +00:00)
Checking length and one sample record
print(len(train_padded_split_data))
print(len(test_padded_split_data))
22895 5724 time: 1.62 ms (started: 2021-11-21 16:08:59 +00:00)
print(train_padded_split_data[2])
print()
print(X_train[2])
[ 0 0 0 0 0 0 0 0 0 0 0 0 0 0
0 0 0 0 0 0 0 0 0 0 0 0 0 0
0 0 0 0 0 0 0 0 0 0 0 0 0 0
0 0 0 0 0 0 0 0 0 0 0 0 0 0
0 0 0 0 0 0 0 0 0 0 0 0 0 0
0 0 0 0 0 0 0 0 0 0 0 0 0 0
0 0 0 0 0 0 0 0 0 5440 1861 105 533 504
150 163]
stripper failing school she's working self through
time: 2.25 ms (started: 2021-11-21 16:08:59 +00:00)
Checking basic statistic about the tokenizer
#Checking basic statistic about the tokenizer
print(tokenizer_split_data.word_counts)
print(tokenizer_split_data.document_count)
print(tokenizer_split_data.word_index)
print(tokenizer_split_data.word_docs)
OrderedDict([('how', 676), ('trump', 1118), ('really', 202), ('feels', 50), ('about', 903), ('queer', 42), ('people', 314), ('explained', 6), ('in', 3616), ('one', 449), ('sentence', 18), ('egyptian', 7), ('death', 184), ('for', 2863), ('soccer', 23), ('fans', 87), ('puts', 43), ("president's", 18), ('iron', 9), ('grip', 3), ('to', 7259), ('the', 4268), ('test', 42), ('stripper', 5), ('failing', 19), ('school', 210), ("she's", 59), ('working', 61), ('self', 161), ('through', 152), ('texas', 65), ('attorney', 18), ('general', 36), ('ken', 10), ('paxton', 3), ('indicted', 10), ('congresswoman', 5), ('fights', 10), ('gun', 117), ('control', 78), ('because', 81), ('she', 190), ('almost', 38), ('lost', 57), ('her', 339), ('life', 312), ('violence', 77), ('lana', 3), ('del', 7), ('rey', 2), ('and', 1587), ('stevie', 4), ('nicks', 4), ('cast', 26), ('a', 2554), ('joint', 8), ('musical', 16), ('spell', 4), ('on', 2139), ('upcoming', 29), ('album', 37), ('food', 110), ('insecurity', 1), ('inactivity', 1), ('are', 543), ('driving', 37), ('obesity', 8), ('epidemic', 17), ("here's", 134), ('reminder', 19), ('of', 5060), ('far', 47), ('donald', 366), ('has', 501), ('flip', 14), ('flopped', 1), ('health', 165), ('care', 108), ('disgruntled', 3), ('liberals', 10), ('publishing', 5), ('at', 1066), ('furious', 11), ('pace', 9), ('period', 13), ('time', 375), ('which', 38), ('parents', 150), ('proud', 36), ('much', 120), ('child', 142), ('can', 398), ('eat', 37), ('quickly', 18), ('dwindling', 2), ('j', 38), ('k', 40), ('rowling', 4), ('revealed', 12), ('be', 735), ('pseudonym', 1), ('newt', 9), ('gingrich', 13), ('supermodel', 1), ('stephanie', 4), ('seymour', 3), ('arrested', 25), ('charged', 25), ('with', 1568), ('drunken', 6), ('funniest', 22), ('parenting', 22), ('tweets', 57), ('what', 515), ('moms', 33), ('dads', 14), ('said', 31), ('twitter', 79), ('this', 783), ('week', 119), ('personal', 61), ('memory', 21), ('former', 89), ('governor', 40), ('mario', 7), ('cuomo', 10), ('muslims', 22), ('respond', 9), ('hateful', 7), ('protests', 22), ('voter', 37), ('registration', 3), ('drives', 11), ('fbi', 59), ('arrests', 10), ('brother', 31), ('san', 30), ('bernardino', 7), ('terrorist', 14), ('2', 170), ('others', 20), ('marriage', 81), ('fraud', 18), ('charges', 21), ('alcohol', 8), ('only', 224), ('thing', 110), ('making', 100), ('operating', 6), ('heavy', 9), ('machinery', 1), ('bearable', 1), ('confusing', 6), ('roadside', 2), ('memorial', 18), ('features', 23), ('bicycle', 2), ('rotary', 1), ('telephone', 4), ('jug', 2), ('some', 152), ('kind', 52), ('is', 1380), ('gates', 19), ('foundation', 16), ('investing', 4), ('abuse', 48), ('palestinian', 18), ('prisoners', 11), ('5', 248), ('u', 421), ('s', 397), ('terrorism', 18), ('related', 17), ('crimes', 20), ('caller', 5), ('enters', 31), ('remote', 11), ('backwaters', 1), ('1', 123), ('800', 10), ('automated', 4), ('messaging', 2), ('system', 52), ('clever', 8), ('way', 248), ('starbucks', 12), ('customers', 29), ('insisting', 2), ("'black", 11), ('lives', 62), ("matter'", 2), ('heard', 26), ('when', 257), ('your', 533), ('clicks', 1), ('into', 484), ('place', 65), ('advisors', 6), ('tell', 100), ('cruz', 66), ('stick', 16), ('just', 543), ('attacking', 11), ('all', 515), ('women', 259), ('mom', 168), ('thinks', 85), ("you'd", 6), ('enjoy', 21), ('restaurant', 59), ("can't", 202), ('remember', 40), ('name', 84), ('right', 170), ('now', 337), ('ultimate', 20), ('livingston', 2), ('montana', 9), ('road', 39), ('trip', 54), ('playlist', 6), ('cuba', 21), ('an', 294), ('ambitious', 5), ('plan', 132), ('protect', 32), ('its', 136), ('environment', 13), ('from', 1052), ('tourists', 10), ('raid', 13), ('nacho', 4), ('supremacist', 7), ('compound', 1), ('uncovers', 2), ('guacamole', 4), ('materials', 2), ('jeb', 29), ('bush', 130), ('calls', 143), ('crackdown', 11), ('sanctuary', 6), ('cities', 28), ('immigration', 50), ('ronnie', 1), ('wood', 4), ('says', 405), ('he', 558), ('worried', 49), ('it', 665), ('was', 269), ("'time", 1), ('say', 178), ("goodbye'", 1), ('after', 802), ('cancer', 60), ('diagnosis', 5), ('if', 275), ('holiday', 50), ('stress', 27), ('disease', 20), ('virus', 12), ('expectations', 11), ("don't", 137), ('blame', 22), ("'a'", 1), ('but', 192), ("'pretty", 5), ('little', 158), ("liars'", 2), ('ending', 16), ('7', 124), ('seasons', 6), ('smart', 18), ('alice', 2), ('vote', 82), ('democratic', 54), ('senators', 28), ('neil', 12), ('gorsuch', 10), ('refused', 5), ('meet', 71), ('them', 138), ('milestone', 2), ('i', 257), ("wasn't", 17), ('ready', 64), ('my', 257), ('first', 376), ('man', 1102), ('crush', 6), ('james', 84), ('garner', 5), ('lawmaker', 15), ('gives', 69), ('scott', 54), ('pruitt', 17), ('brutal', 8), ('reality', 45), ('check', 36), ('during', 194), ('senate', 113), ('hearing', 30), ('dies', 60), ('peacefully', 4), ('his', 535), ('sleep', 54), ('that', 704), ('awkward', 20), ('age', 59), ('where', 158), ('no', 348), ('cares', 5), ("he's", 145), ('constantly', 14), ('bhutanese', 1), ('believe', 65), ('pharmacy', 2), ('already', 102), ('stocking', 2), ('stuff', 28), ('lhabab', 1), ('duchen', 1), ('stripping', 2), ('access', 25), ('indirectly', 1), ('ensures', 3), ('republican', 83), ('success', 38), ('sens', 1), ('mccain', 28), ('graham', 17), ("trump's", 329), ('order', 50), ('could', 250), ('become', 46), ("'self", 3), ('inflicted', 2), ("wound'", 1), ('terror', 26), ('fight', 93), ('saudis', 3), ('insist', 4), ('missing', 70), ('journalist', 18), ('dismembered', 6), ('before', 243), ('left', 65), ('consulate', 3), ('usher', 5), ('put', 68), ('shirt', 26), ('back', 285), ('apple', 44), ('loses', 38), ('patent', 6), ('lawsuit', 31), ('university', 37), ('wisconsin', 16), ('madison', 4), ('espn', 8), ('analyst', 7), ('blames', 18), ("'liberal", 1), ("media'", 2), ("'war", 4), ("football'", 1), ('ryan', 67), ('gosling', 2), ('sneaks', 6), ('past', 70), ('paparazzi', 2), ('full', 85), ('body', 103), ('red', 64), ('carpet', 25), ('camouflage', 1), ('garden', 12), ('state', 185), ('poor', 35), ("fuck's", 1), ('favorite', 50), ('movie', 95), ('area', 410), ('waitress', 12), ('gets', 160), ('chance', 39), ('cincinnati', 3), ('campus', 21), ('police', 197), ('had', 102), ('surge', 13), ('citations', 1), ('against', 170), ('black', 215), ('motorists', 1), ('pedestrians', 2), ('visual', 8), ('history', 99), ("'the", 184), ("nutcracker'", 1), ('100', 50), ('photos', 84), ('bernie', 69), ('sanders', 91), ('shows', 111), ('reagan', 6), ('destroyed', 12), ('middle', 79), ('class', 71), ('fired', 28), ('aide', 15), ('campaign', 175), ('chair', 27), ('should', 181), ('resign', 14), ('responsible', 14), ('plagiarism', 3), ('homeland', 12), ('security', 89), ('director', 52), ('releases', 94), ('list', 67), ('terrorists', 11), ('who', 512), ('have', 448), ('balls', 7), ('attack', 102), ('show', 180), ('fire', 93), ('smoke', 7), ('engulfing', 1), ('ankara', 1), ('district', 9), ('deadly', 29), ('car', 86), ('bomb', 16), ('pool', 24), ('owner', 57), ('bathing', 4), ('suit', 26), ('touched', 7), ('penis', 16), ('you', 801), ('borrow', 3), ('kendrick', 8), ('lamar', 11), ('won', 19), ('pulitzer', 7), ("'damn", 1), ("'", 319), ('journalism', 6), ('video', 184), ('heartbreak', 4), ('loving', 16), ('someone', 61), ("alzheimer's", 12), ('gop', 220), ('rep', 21), ('blake', 11), ('farenthold', 3), ('most', 213), ('americans', 223), ('afford', 16), ('minor', 11), ('emergency', 27), ('art', 66), ('professor', 15), ('convincing', 5), ('fake', 35), ('new', 1344), ('sympathetic', 3), ('alarm', 9), ('clock', 15), ('lets', 40), ('look', 143), ('tiny', 21), ('home', 178), ('dumpster', 3), ('carlos', 4), ('rosario', 1), ('educates', 1), ('graduates', 15), ('deletes', 2), ("'rhymezone", 1), ("com'", 1), ('internet', 54), ('hillary', 162), ('clinton', 251), ('mouthing', 3), ('along', 20), ('presidential', 86), ('oath', 8), ('read', 46), ('live', 112), ('updates', 10), ('vice', 19), ('debate', 125), ('jim', 26), ('jordan', 13), ('spends', 51), ('demanding', 6), ('michael', 78), ('cohen', 19), ('accept', 17), ('covering', 7), ('up', 749), ('sexual', 118), ('ohio', 25), ('wrestlers', 2), ('cartoon', 8), ('peppers', 5), ('menu', 10), ('foreboding', 1), ('warning', 27), ('would', 229), ('dare', 6), ('spicy', 7), ('entrees', 1), ('freud', 1), ('forget', 16), ('domhnall', 1), ('gleeson', 1), ("'nasty", 2), ('piece', 25), ("work'", 3), ('hux', 1), ("today's", 14), ('brightest', 3), ('actors', 16), ('hugh', 4), ('laurie', 5), ('returns', 40), ('doctor', 39), ('game', 105), ('except', 13), ('none', 4), ('answers', 11), ("'broad", 4), ("city'", 10), ('ladies', 8), ('spa', 2), ('day', 398), ("doesn't", 160), ('go', 139), ('well', 56), ("'that", 8), ('seems', 13), ('soon', 32), ('audited', 1), ('epa', 32), ('pick', 35), ('leading', 34), ('foe', 3), ('clean', 30), ('water', 70), ('laws', 20), ("women's", 57), ('group', 80), ('uses', 35), ('drones', 6), ('deliver', 11), ('abortion', 49), ('pills', 11), ('scientific', 10), ('research', 26), ('huge', 50), ('victory', 21), ('jeff', 40), ('bridges', 5), ('seated', 6), ('directly', 16), ('behind', 85), ('support', 92), ('column', 6), ('golden', 27), ('globes', 15), ('erupts', 9), ('rally', 38), ('supporters', 40), ('clash', 3), ('protesting', 9), ('leaders', 50), ('aaron', 7), ('hernandez', 5), ('suicide', 38), ('highlights', 8), ('systemic', 1), ('problem', 56), ('massachusetts', 6), ('jails', 2), ('prisons', 4), ('white', 294), ('house', 303), ('carefully', 10), ('screening', 5), ('any', 100), ('town', 59), ('hall', 41), ('questions', 60), ('address', 32), ('obama', 307), ('as', 678), ("'mein", 1), ("führer'", 1), ('responds', 26), ('charlottesville', 17), ('quote', 4), ('nelson', 9), ('mandela', 7), ('dog', 124), ('sick', 41), ('being', 216), ('compared', 6), ('old', 336), ('hollywood', 54), ("boys'", 2), ('club', 28), ('supports', 7), ('casey', 3), ('affleck', 13), ('total', 18), ('disgrace', 1), ('guy', 139), ('sunk', 1), ('half', 59), ('court', 148), ('shot', 65), ('without', 104), ('touching', 16), ('ball', 25), ('emojisinthewild', 1), ('taking', 69), ('over', 442), ('instagram', 23), ('bill', 204), ('murray', 4), ('even', 137), ('charming', 2), ('while', 161), ('trying', 111), ('bribe', 2), ('umpires', 1), ('insufferable', 4), ('8', 107), ('year', 440), ("won't", 110), ('stop', 144), ('chanting', 6), ("'romney'", 1), ('asks', 82), ('anyone', 40), ("who's", 27), ('serious', 24), ('breaking', 69), ('big', 178), ('banks', 11), ('him', 234), ('corner', 14), ('canal', 3), ('bowery', 1), ('midnight', 9), ('hollande', 1), ('valls', 1), ('macron', 4), ('article', 20), ('49', 6), ('3', 208), ('france', 17), ('tries', 30), ('harder', 17), ('reform', 39), ("kids'", 8), ('behavior', 15), ('linked', 16), ("moms'", 1), ('acetaminophen', 1), ('use', 83), ('pregnancy', 24), ('busybody', 2), ('fireman', 3), ('ruins', 11), ('attempt', 34), ('comic', 14), ('book', 84), ('superrman', 1), ('impervious', 2), ('copyediting', 1), ('jedi', 3), ('chipmunks', 1), ('lightsabers', 2), ('universe', 21), ('wins', 60), ('sign', 45), ('explains', 31), ("highway's", 1), ('dangers', 13), ('photo', 75), ('ipad', 4), ('oblivious', 1), ('badass', 9), ('looks', 72), ('best', 195), ('moment', 45), ('entire', 103), ('season', 115), ('deputy', 14), ('by', 836), ('heavily', 5), ('redacted', 1), ('farewell', 8), ('card', 35), ('bureau', 6), ('coworkers', 20), ('bitcoin', 6), ('plunge', 5), ('reveals', 116), ('possible', 41), ('vulnerabilities', 3), ('crazy', 25), ('imaginary', 2), ('money', 103), ('fame', 15), ('fortune', 5), ('shaped', 12), ('business', 94), ('artist', 34), ('late', 45), ('ceo', 76), ('out', 740), ('coffee', 40), ('vain', 3), ('walking', 40), ('away', 115), ('higher', 24), ('calling', 23), ('or', 182), ('more', 467), ('cops', 43), ('been', 120), ('shootings', 15), ("there's", 44), ('work', 160), ('done', 28), ('reverses', 3), ('course', 30), ('offers', 50), ('dates', 9), ('nato', 14), ('talks', 65), ('coed', 1), ('rec', 2), ('softball', 2), ('team', 72), ('having', 97), ('trouble', 16), ('finding', 40), ('enough', 112), ('hyper', 1), ('competitive', 4), ('men', 96), ('ruin', 13), ('experience', 45), ('archaeologists', 12), ('apologize', 11), ('murdering', 6), ('last', 182), ('remaining', 13), ('neanderthal', 1), ('fit', 25), ('crazed', 6), ('bloodlust', 1), ('apartment', 39), ('weed', 13), ('table', 40), ("'piggies'", 1), ('written', 16), ('blood', 44), ('clouds', 3), ('clue', 7), ('shocking', 12), ('murder', 45), ('six', 35), ('angels', 5), ("'steak", 2), ("onion'", 8), ('potato', 8), ('chips', 6), ('taste', 16), ('disturbingly', 5), ('like', 381), ('steak', 5), ('onions', 2), ('6', 138), ('facts', 16), ("didn't", 88), ('know', 190), ('trade', 35), ('they', 246), ('affect', 7), ('why', 405), ('writers', 9), ('must', 92), ('surprised', 26), ('crowd', 30), ('shocked', 26), ('unhinged', 7), ('dangles', 1), ('baby', 126), ('truman', 4), ('balcony', 7), ('george', 67), ('takei', 5), ('apologizes', 41), ('clarence', 3), ('thomas', 14), ("'clown", 1), ("blackface'", 1), ('wedding', 90), ('videographer', 1), ('clearly', 50), ('shooting', 105), ('side', 43), ('project', 24), ('ceremony', 26), ('teary', 6), ('eyed', 20), ('student', 80), ('loan', 11), ('officers', 18), ('proudly', 6), ('watch', 192), ('200', 25), ('000', 163), ('asset', 3), ('college', 115), ('prefrontal', 1), ('cortex', 2), ('admits', 86), ('possibly', 7), ('filter', 4), ('impulsive', 2), ('comments', 30), ('coming', 77), ('rest', 30), ('brain', 25), ('commercial', 29), ('fun', 52), ('camera', 24), ('than', 278), ('humanly', 1), ('party', 159), ('ragtag', 1), ('assembly', 5), ('friends', 91), ('latest', 53), ('concludes', 7), ('candidates', 46), ('wrestling', 6), ('squealing', 1), ('pig', 9), ('ground', 22), ('slaughtering', 4), ('exhausted', 21), ('explain', 24), ('halloween', 33), ('costume', 18), ('umpteenth', 1), ('conservative', 19), ('newspaper', 16), ('editorial', 6), ('boards', 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22895
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3749, 'checking': 3750, 'athletes': 3751, 'direction': 3752, '38': 3753, 'floating': 3754, 'pretend': 3755, 'inch': 3756, 'nevada': 3757, 'twins': 3758, "woman'": 3759, 'gina': 3760, 'rapper': 3761, 'mentions': 3762, 'donors': 3763, 'jews': 3764, 'stream': 3765, 'charlotte': 3766, 'kfc': 3767, 'eight': 3768, 'simpson': 3769, 'mistake': 3770, 'bullying': 3771, '911': 3772, 'billboard': 3773, 'master': 3774, 'efforts': 3775, 'lago': 3776, 'platform': 3777, 'divided': 3778, "'love": 3779, 'zone': 3780, 'concern': 3781, 'austin': 3782, 'bastard': 3783, 'added': 3784, 'convinced': 3785, 'easier': 3786, 'encourages': 3787, 'southern': 3788, 'solidarity': 3789, 'sexism': 3790, 'supporter': 3791, 'concerns': 3792, 'coal': 3793, 'react': 3794, 'accuses': 3795, 'bright': 3796, 'feared': 3797, 'sanctions': 3798, 'virgin': 3799, 'journal': 3800, 'fiorina': 3801, 'restrictions': 3802, 'startup': 3803, 'faa': 3804, 'jerusalem': 3805, 'migrant': 3806, 'daring': 3807, 'jill': 3808, 'notre': 3809, 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3870, 'vladimir': 3871, 'jazz': 3872, 'memories': 3873, 'butt': 3874, 'forms': 3875, 'sikh': 3876, 'parliament': 3877, 'explore': 3878, 'conversations': 3879, 'nominees': 3880, 'reader': 3881, 'shelves': 3882, 'starving': 3883, 'viewer': 3884, 'makeover': 3885, 'harrowing': 3886, 'melt': 3887, 'wonder': 3888, 'commission': 3889, 'compete': 3890, 'corpse': 3891, 'denying': 3892, 'uncle': 3893, '72': 3894, 'greater': 3895, 'dorm': 3896, '95': 3897, 'cd': 3898, 'option': 3899, 'sunglasses': 3900, 'constant': 3901, 'grandpa': 3902, 'twist': 3903, 'flat': 3904, 'transforms': 3905, 'ended': 3906, 'breath': 3907, "wife's": 3908, 'bucks': 3909, 'tickets': 3910, 'videos': 3911, 'considers': 3912, 'horrific': 3913, 'sxsw': 3914, 'usda': 3915, 'loudly': 3916, 'resignation': 3917, 'intact': 3918, "'do": 3919, 'pardon': 3920, 'hire': 3921, 'tsarnaev': 3922, 'galaxy': 3923, 'ally': 3924, 'unions': 3925, 'shared': 3926, 'bitch': 3927, 'edwards': 3928, 'couch': 3929, 'increased': 3930, "wednesday's": 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5243, 'preview': 5244, 'gotten': 5245, 'alcoholic': 5246, 'spiritual': 5247, 'awake': 5248, 'panicking': 5249, 'indefinitely': 5250, 'packing': 5251, 'buddy': 5252, '2009': 5253, 'abusive': 5254, 'prank': 5255, 'virtual': 5256, 'bile': 5257, 'reflection': 5258, 'pm': 5259, 'savings': 5260, 'exec': 5261, 'stray': 5262, 'bursts': 5263, 'retirees': 5264, 'purpose': 5265, 'handed': 5266, 'removed': 5267, 'rip': 5268, 'preschool': 5269, 'trumpcare': 5270, 'cigarette': 5271, 'heartfelt': 5272, 'ourselves': 5273, "brazil's": 5274, 'print': 5275, 'oxford': 5276, "'most": 5277, 'whatsapp': 5278, 'op': 5279, 'brooks': 5280, 'monologue': 5281, 'deaf': 5282, '33': 5283, 'visibly': 5284, '46': 5285, 'theresa': 5286, '2010': 5287, 'payment': 5288, 'pics': 5289, 'beliefs': 5290, 'replaced': 5291, 'narrowly': 5292, 'reunite': 5293, 'wreck': 5294, 'humor': 5295, 'vehicle': 5296, "day's": 5297, 'vagina': 5298, 'doj': 5299, 'psychic': 5300, 'explaining': 5301, 'ear': 5302, 'leonardo': 5303, 'dicaprio': 5304, 'flood': 5305, 'justices': 5306, 'nordstrom': 5307, 'lock': 5308, 'payments': 5309, 'psychology': 5310, 'singapore': 5311, "mccain's": 5312, 'exposes': 5313, 'cap': 5314, 'below': 5315, 'shy': 5316, 'patrol': 5317, "americans'": 5318, "users'": 5319, "bannon's": 5320, 'hut': 5321, '250': 5322, 'carrie': 5323, 'approved': 5324, 'bookstore': 5325, 'lewis': 5326, 'protected': 5327, 'susan': 5328, 'rebuild': 5329, 'solemnly': 5330, 'crushes': 5331, 'whites': 5332, 'alike': 5333, 'legislature': 5334, 'lobbyist': 5335, 'slope': 5336, 'eclipse': 5337, 'braces': 5338, 'tsa': 5339, 'goldfish': 5340, 'rocky': 5341, 'earn': 5342, 'bullet': 5343, "post'": 5344, 'weighing': 5345, 'piss': 5346, 'caucus': 5347, 'regulations': 5348, 'supplies': 5349, 'roads': 5350, 'minds': 5351, 'bean': 5352, 'knocking': 5353, 'cent': 5354, 'gillibrand': 5355, 'spike': 5356, 'marry': 5357, 'troll': 5358, "jedi'": 5359, 'stumbles': 5360, 'sigh': 5361, 'danger': 5362, 'topic': 5363, 'reopens': 5364, 'fargo': 5365, 'recipients': 5366, 'criticizes': 5367, 'sides': 5368, 'solved': 5369, 'consuming': 5370, 'unity': 5371, 'flights': 5372, 'wind': 5373, 'defending': 5374, "'girls'": 5375, 'cease': 5376, 'loyalty': 5377, 'selection': 5378, "pence's": 5379, 'dragging': 5380, 'ensure': 5381, 'loyal': 5382, 'embassy': 5383, 'shopper': 5384, 'packed': 5385, 'delta': 5386, 'christina': 5387, 'clad': 5388, 'dhs': 5389, "senate's": 5390, 'washed': 5391, 'martial': 5392, 'teaser': 5393, 'bipartisan': 5394, 'forbidden': 5395, 'rahm': 5396, 'analytica': 5397, 'novelty': 5398, 'devastating': 5399, 'skip': 5400, 'wal': 5401, 'burgers': 5402, 'betrayal': 5403, "ain't": 5404, 'underwater': 5405, 'interrupted': 5406, 'redemption': 5407, 'executed': 5408, 'cheering': 5409, 'peyton': 5410, 'restaurants': 5411, 'salesman': 5412, 'edited': 5413, 'pepsi': 5414, 'exposure': 5415, 'feds': 5416, 'abc': 5417, 'reverse': 5418, 'runway': 5419, 'jackpot': 5420, 'stirs': 5421, 'soft': 5422, 'harassed': 5423, 'glorious': 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'dangerously': 5541, 'expands': 5542, 'confirmed': 5543, 'creature': 5544, 'myanmar': 5545, 'photography': 5546, "states'": 5547, 'haircut': 5548, 'amazed': 5549, 'salmon': 5550, 'deputies': 5551, 'fucks': 5552, 'cutest': 5553, 'accessible': 5554, 'expansion': 5555, 'protection': 5556, 'advisor': 5557, 'blacks': 5558, 'ongoing': 5559, "'time'": 5560, 'ward': 5561, "'gay": 5562, 'landmark': 5563, 'assaulting': 5564, 'massage': 5565, 'diamond': 5566, 'hostage': 5567, 'freed': 5568, 'philly': 5569, 'appointment': 5570, 'piano': 5571, 'empathy': 5572, 'acknowledge': 5573, 'buffalo': 5574, 'commuters': 5575, '900': 5576, 'dispute': 5577, 'hangs': 5578, 'involve': 5579, "swift's": 5580, 'threw': 5581, 'battling': 5582, 'hilary': 5583, 'cackling': 5584, 'lovely': 5585, 'chilean': 5586, 'psa': 5587, 'hungover': 5588, 'demise': 5589, 'core': 5590, 'smithsonian': 5591, 'prompts': 5592, 'chooses': 5593, 'fairy': 5594, 'tales': 5595, 'tragically': 5596, 'wolves': 5597, 'fines': 5598, 'exists': 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6724, 'tenure': 6725, 'commander': 6726, 'stared': 6727, 'addressed': 6728, 'hawaiian': 6729, 'draw': 6730, "coworker's": 6731, 'temp': 6732, 'watermelon': 6733, 'laughing': 6734, 'cameraman': 6735, 'payday': 6736, 'girlfriends': 6737, 'lounge': 6738, 'organize': 6739, "season's": 6740, 'wash': 6741, 'marital': 6742, 'pharmaceutical': 6743, 'outlet': 6744, 'vogue': 6745, "'america": 6746, 'bombs': 6747, 'slammed': 6748, 'salvador': 6749, "applebee's": 6750, 'addict': 6751, 'hawking': 6752, 'henry': 6753, 'restricting': 6754, 'concealed': 6755, 'florence': 6756, 'manufacturing': 6757, 'dust': 6758, 'guides': 6759, 'cemetery': 6760, 'hmo': 6761, 'pumps': 6762, 'sized': 6763, 'slice': 6764, 'barriers': 6765, 'walkout': 6766, 'jonas': 6767, 'slick': 6768, 'nabs': 6769, 'unleash': 6770, 'jenkins': 6771, 'subtle': 6772, 'expires': 6773, 'knees': 6774, "girl'": 6775, 'partnership': 6776, "romney's": 6777, 'wages': 6778, 'teddy': 6779, 'outrageous': 6780, 'mocking': 6781, 'ruby': 6782, 'duncan': 6783, 'culinary': 6784, 'stare': 6785, 'cans': 6786, 'expose': 6787, "'like": 6788, 'leftovers': 6789, '2000': 6790, 'receiving': 6791, "administration's": 6792, 'cents': 6793, 'counting': 6794, 'xi': 6795, 'mailbox': 6796, 'sacrifice': 6797, 'maria': 6798, 'application': 6799, 'prospective': 6800, 'maroon': 6801, 'ninja': 6802, 'sticks': 6803, 'suing': 6804, 'juicy': 6805, 'infuriating': 6806, 'waist': 6807, 'behaviors': 6808, 'patriot': 6809, 'spoon': 6810, 'environmentalists': 6811, 'inspirational': 6812, 'undergo': 6813, 'representatives': 6814, 'mild': 6815, 'seemed': 6816, 'stations': 6817, 'sibling': 6818, 'deleted': 6819, 'anchors': 6820, 'ode': 6821, 'chimpanzees': 6822, 'cyclist': 6823, 'blitzer': 6824, 'barry': 6825, '47': 6826, 'achieves': 6827, 'replacing': 6828, 'vigil': 6829, 'maya': 6830, 'angelou': 6831, 'stirring': 6832, 'emerging': 6833, 'czar': 6834, 'beached': 6835, 'arrangement': 6836, 'participate': 6837, 'maddow': 6838, 'naomi': 6839, 'merrick': 6840, 'task': 6841, 'declines': 6842, 'nun': 6843, 'chills': 6844, 'obsolete': 6845, 'calendar': 6846, 'pardons': 6847, 'crocodile': 6848, 'maintains': 6849, 'portal': 6850, 'outage': 6851, 'intellectual': 6852, 'pitbull': 6853, 'supremacists': 6854, 'storyline': 6855, 'earns': 6856, 'soulmate': 6857, 'cafe': 6858, '130': 6859, 'hermione': 6860, 'waking': 6861, 'altar': 6862, 'tobacco': 6863, 'invite': 6864, 'momentum': 6865, 'husbands': 6866, 'cherub': 6867, 'badge': 6868, 'clergy': 6869, 'rosie': 6870, 'industrial': 6871, 'beatles': 6872, 'narrow': 6873, 'knocks': 6874, 'removal': 6875, 'impersonator': 6876, 'harvard': 6877, 'stressing': 6878, 'october': 6879, 'liar': 6880, 'unsustainable': 6881, 'treating': 6882, 'lately': 6883, 'brussels': 6884, 'info': 6885, 'diary': 6886, 'kidnapping': 6887, 'peaceful': 6888, 'transfer': 6889, 'mayan': 6890, 'fool': 6891, 'tapes': 6892, 'brags': 6893, 'populism': 6894, 'cousins': 6895, 'resources': 6896, 'capabilities': 6897, 'huntsman': 6898, 'noble': 6899, 'honestly': 6900, 'posted': 6901, 'uncertain': 6902, 'evolutionary': 6903, 'function': 6904, 'chases': 6905, 'escorted': 6906, 'snl': 6907, 'toss': 6908, 'engineering': 6909, 'murderer': 6910, 'roasts': 6911, 'diving': 6912, 'situations': 6913, 'salsa': 6914, 'bangladesh': 6915, 'marries': 6916, 'hijacked': 6917, "bad'": 6918, 'weiner': 6919, 'layer': 6920, 'pillow': 6921, 'ballet': 6922, 'randomly': 6923, "comey's": 6924, 'proving': 6925, 'limited': 6926, 'responders': 6927, '77': 6928, 'socks': 6929, 'proved': 6930, 'thin': 6931, 'breakroom': 6932, 'proposals': 6933, 'vending': 6934, 'gyllenhaal': 6935, 'shutting': 6936, 'mickey': 6937, 'firearm': 6938, 'bragging': 6939, 'changer': 6940, 'polluted': 6941, 'coveted': 6942, 'reuters': 6943, 'pinterest': 6944, 'erotic': 6945, 'privately': 6946, 'someday': 6947, 'philosophy': 6948, 'pabst': 6949, 'lifted': 6950, "8'": 6951, 'damning': 6952, 'sword': 6953, 'armor': 6954, 'swimmer': 6955, "grandmother's": 6956, 'bra': 6957, 'beckham': 6958, 'syndrome': 6959, 'unleashes': 6960, 'inappropriate': 6961, 'historian': 6962, 'ordinary': 6963, 'papers': 6964, 'tightens': 6965, 'vortex': 6966, 'nationalist': 6967, 'redefine': 6968, 'hoax': 6969, 'denied': 6970, 'soil': 6971, 'shits': 6972, 'photograph': 6973, 'internal': 6974, 'description': 6975, 'kremlin': 6976, 'hijab': 6977, '74': 6978, 'bash': 6979, 'fountain': 6980, 'identified': 6981, 'kindergarten': 6982, 'nephew': 6983, 'surf': 6984, 'shaping': 6985, 'containing': 6986, "'fuck": 6987, 'legalization': 6988, 'eliminates': 6989, 'margarita': 6990, 'knight': 6991, 'derails': 6992, 'duvernay': 6993, 'placing': 6994, 'duped': 6995, 'supremacy': 6996, 'overhead': 6997, 'lennon': 6998, 'interrupts': 6999, 'wiretapping': 7000, 'impulse': 7001, 'packs': 7002, 'organs': 7003, 'donor': 7004, 'sensitive': 7005, 'delicate': 7006, 'helpless': 7007, 'keynote': 7008, 'compassion': 7009, "abc's": 7010, 'tolerate': 7011, 'colors': 7012, 'connecting': 7013, 'solving': 7014, 'reduces': 7015, 'porter': 7016, 'neglected': 7017, 'grounds': 7018, 'pigeon': 7019, 'futures': 7020, 'siege': 7021, 'gabrielle': 7022, "tale'": 7023, 'dutch': 7024, 'solitary': 7025, 'honda': 7026, 'midterm': 7027, 'ballots': 7028, "city's": 7029, 'unexpectedly': 7030, 'references': 7031, 'beheading': 7032, 'trainer': 7033, 'pearl': 7034, 'soars': 7035, 'scandals': 7036, 'lowest': 7037, 'lol': 7038, 'adopted': 7039, 'talented': 7040, 'entertaining': 7041, 'filling': 7042, 'nicole': 7043, "'celebrity": 7044, 'broadcast': 7045, 'households': 7046, 'bailiff': 7047, 'chili': 7048, 'tactics': 7049, 'perverted': 7050, 'promising': 7051, 'regional': 7052, 'speeches': 7053, 'typing': 7054, 'psychological': 7055, 'riff': 7056, 'tanker': 7057, 'instructor': 7058, 'harriet': 7059, 'spelling': 7060, 'doodle': 7061, 'reduction': 7062, 'outdoors': 7063, 'aretha': 7064, 'entrepreneurship': 7065, 'violating': 7066, 'coasting': 7067, 'ate': 7068, 'subculture': 7069, 'rove': 7070, 'backstreet': 7071, 'lecture': 7072, 'walter': 7073, 'oral': 7074, 'wanting': 7075, 'hospitalization': 7076, 'category': 7077, "parkinson's": 7078, 'sufferer': 7079, 'sustain': 7080, 'methane': 7081, 'diabetes': 7082, 'galactic': 7083, 'alexandria': 7084, 'terrain': 7085, 'tweeters': 7086, 'kobe': 7087, 'flawed': 7088, 'colony': 7089, 'victorious': 7090, "'guardians": 7091, "galaxy'": 7092, 'odd': 7093, 'pineapple': 7094, 'athlete': 7095, 'comprehensive': 7096, 'rookie': 7097, 'hurricanes': 7098, 'loop': 7099, 'advocate': 7100, 'oasis': 7101, "artist's": 7102, 'assed': 7103, 'louder': 7104, 'critically': 7105, "town's": 7106, 'joseph': 7107, "jesus'": 7108, 'defund': 7109, 'apartments': 7110, 'heal': 7111, 'mc': 7112, 'audubon': 7113, 'lesbians': 7114, 'tits': 7115, 'dreamed': 7116, 'understands': 7117, 'doubled': 7118, 'columnist': 7119, 'indigenous': 7120, 'internship': 7121, 'launched': 7122, 'pine': 7123, 'wows': 7124, "perry's": 7125, 'pastry': 7126, 'insult': 7127, 'frustration': 7128, 'slamming': 7129, '62': 7130, 'airing': 7131, 'pier': 7132, 'teenager': 7133, "chicago's": 7134, 'swearing': 7135, 'shore': 7136, 'christians': 7137, 'bonds': 7138, '20th': 7139, 'masculinity': 7140, 'tosses': 7141, 'curvy': 7142, 'shady': 7143, 'equity': 7144, 'crowdfunding': 7145, 'overtime': 7146, 'dome': 7147, 'withdraws': 7148, 'hanukkah': 7149, 'avid': 7150, 'anna': 7151, 'paints': 7152, 'productivity': 7153, 'hoffman': 7154, 'prostitute': 7155, 'criticize': 7156, 'panthers': 7157, 'hugging': 7158, 'vase': 7159, '30th': 7160, 'alexander': 7161, 'pharrell': 7162, 'complimentary': 7163, 'skyscraper': 7164, 'sexiest': 7165, 'crosshairs': 7166, 'jerk': 7167, 'crave': 7168, 'steam': 7169, "game'": 7170, 'résumé': 7171, 'jets': 7172, "soon'": 7173, 'cub': 7174, 'cope': 7175, 'prays': 7176, 'spree': 7177, 'delighted': 7178, 'prosthetic': 7179, 'forgets': 7180, "'smart'": 7181, 'ease': 7182, 'compulsive': 7183, 'tablets': 7184, 'compromise': 7185, 'streak': 7186, 'shield': 7187, 'dwayne': 7188, 'abuser': 7189, 'deems': 7190, 'dem': 7191, 'mayhem': 7192, '97': 7193, "heart'": 7194, 'broker': 7195, 'imagines': 7196, 'tongue': 7197, 'boosts': 7198, 'hanes': 7199, 'aware': 7200, 'dipping': 7201, 'violently': 7202, 'buyers': 7203, 'transforming': 7204, 'bonus': 7205, 'somalia': 7206, 'shock': 7207, 'suspend': 7208, 'rupaul': 7209, 'rides': 7210, 'carlson': 7211, 'carcass': 7212, 'hunting': 7213, 'leno': 7214, 'staples': 7215, 'spreads': 7216, 'apologizing': 7217, 'nyt': 7218, 'laughs': 7219, 'politically': 7220, 'alito': 7221, 'longingly': 7222, "'frozen'": 7223, '96': 7224, 'temporary': 7225, 'monica': 7226, 'lauer': 7227, 'escaped': 7228, 'toronto': 7229, 'ira': 7230, 'offensive': 7231, 'warned': 7232, 'emoji': 7233, 'wikipedia': 7234, 'rupert': 7235, 'murdoch': 7236, 'heavenly': 7237, 'exxon': 7238, 'struck': 7239, "they'd": 7240, 'undergoes': 7241, "squad'": 7242, 'foiled': 7243, "years'": 7244, 'anecdote': 7245, 'sponsors': 7246, 'extremist': 7247, 'lip': 7248, "coworkers'": 7249, 'skipping': 7250, 'shops': 7251, 'gonna': 7252, 'halftime': 7253, 'amsterdam': 7254, 'asserts': 7255, 'giamatti': 7256, 'manufacturers': 7257, 'license': 7258, 'backfires': 7259, "'top": 7260, 'pug': 7261, 'wikileaks': 7262, 'petraeus': 7263, 'facility': 7264, 'surrounding': 7265, 'bts': 7266, 'bragged': 7267, 'defamation': 7268, 'vermont': 7269, "'our": 7270, 'negotiating': 7271, 'rom': 7272, 'stanley': 7273, 'ships': 7274, 'bystanders': 7275, 'metallica': 7276, 'luxury': 7277, "'kind": 7278, 'bland': 7279, 'cleaned': 7280, 'loft': 7281, 'sucks': 7282, 'ballistic': 7283, 'harsh': 7284, 'claire': 7285, 'anticipated': 7286, 'fidel': 7287, 'jenny': 7288, 'mccarthy': 7289, 'slumber': 7290, 'landscape': 7291, 'gaps': 7292, 'pour': 7293, 'listener': 7294, 'trainee': 7295, 'snapchat': 7296, 'mourners': 7297, 'radar': 7298, 'reminded': 7299, "myanmar's": 7300, 'walked': 7301, 'wiped': 7302, 'ski': 7303, 'ptsd': 7304, 'tours': 7305, 'collapsed': 7306, 'todd': 7307, "time's": 7308, 'pageant': 7309, 'pancake': 7310, "'he's": 7311, 'professionals': 7312, 'yours': 7313, 'ostracized': 7314, 'urinal': 7315, 'thieves': 7316, 'acquaintance': 7317, 'praised': 7318, 'hides': 7319, 'sparking': 7320, "film's": 7321, 'trap': 7322, 'heroically': 7323, 'verdict': 7324, 'pin': 7325, "'nothing": 7326, 'deadliest': 7327, 'mining': 7328, 'emotions': 7329, "'roseanne'": 7330, 'spinoff': 7331, 'civilian': 7332, 'refer': 7333, 'educate': 7334, 'enable': 7335, 'karl': 7336, "rock'": 7337, 'bagel': 7338, "fuckin'": 7339, 'metaphor': 7340, 'reversal': 7341, 'ivy': 7342, 'costing': 7343, 'hunter': 7344, "senator's": 7345, 'begs': 7346, 'invented': 7347, 'tossed': 7348, 'sat': 7349, 'whistleblower': 7350, "grace'": 7351, 'separation': 7352, "actually'": 7353, 'hippie': 7354, 'habitat': 7355, 'lloyd': 7356, 'hydraulic': 7357, 'animated': 7358, 'males': 7359, "thing'": 7360, 'freezer': 7361, 'inclusion': 7362, 'widespread': 7363, 'ethnic': 7364, 'motorcade': 7365, "prince's": 7366, 'empowering': 7367, 'poker': 7368, 'escalates': 7369, 'provider': 7370, 'inevitable': 7371, 'archbishop': 7372, 'kidnapped': 7373, 'cuff': 7374, 'peas': 7375, 'undo': 7376, 'forehead': 7377, 'starr': 7378, 'hailed': 7379, 'bel': 7380, 'marines': 7381, 'nepal': 7382, "democrats'": 7383, 'plain': 7384, 'audition': 7385, 'hummus': 7386, 'sweeping': 7387, 'refusal': 7388, 'controls': 7389, 'tunnels': 7390, 'flee': 7391, 'wives': 7392, 'ant': 7393, 'crushed': 7394, 'associated': 7395, 'persona': 7396, 'tray': 7397, 'desktop': 7398, 'aguilera': 7399, 'posthumous': 7400, 'temperature': 7401, "'queer": 7402, 'nativity': 7403, 'mosul': 7404, 'baskin': 7405, 'sewage': 7406, 'rodriguez': 7407, 'outreach': 7408, "'would": 7409, 'sharp': 7410, 'marked': 7411, 'rooms': 7412, 'faked': 7413, 'silly': 7414, "'back": 7415, 'matches': 7416, 'reminding': 7417, "candidates'": 7418, 'pyeongchang': 7419, 'universal': 7420, 'tracy': 7421, 'delaying': 7422, 'willed': 7423, 'cherry': 7424, 'boycott': 7425, 'grim': 7426, 'spreading': 7427, 'needle': 7428, 'unconscious': 7429, 'apparent': 7430, 'plead': 7431, 'ripped': 7432, 'donating': 7433, 'coping': 7434, "correspondents'": 7435, 'shrink': 7436, 'seasonal': 7437, 'allergies': 7438, 'labels': 7439, 'patiently': 7440, 'minnie': 7441, 'blanket': 7442, 'coalition': 7443, 'mcconaughey': 7444, 'poised': 7445, 'papa': 7446, 'grass': 7447, 'jazeera': 7448, 'boundaries': 7449, 'rejecting': 7450, 'doggy': 7451, 'exam': 7452, 'representative': 7453, 'begging': 7454, 'alleging': 7455, "'em": 7456, 'screw': 7457, 'overwhelmed': 7458, 'suv': 7459, 'uncertainty': 7460, 'decreased': 7461, "tv's": 7462, 'laquan': 7463, 'bullied': 7464, "o'": 7465, 'python': 7466, 'genetically': 7467, 'modified': 7468, 'ratio': 7469, '1960s': 7470, 'renews': 7471, 'kitsch': 7472, 'bug': 7473, 'catholics': 7474, 'chill': 7475, 'vets': 7476, 'tribe': 7477, 'fascinating': 7478, 'pill': 7479, 'watchdog': 7480, 'trunk': 7481, 'scenario': 7482, 'frame': 7483, 'lovingly': 7484, 'hundred': 7485, 'thousand': 7486, "'oh": 7487, 'circling': 7488, "crime'": 7489, 'tapper': 7490, 'acquired': 7491, 'investors': 7492, 'spokesman': 7493, 'judicial': 7494, 'merge': 7495, 'detailed': 7496, 'leaning': 7497, 'sucked': 7498, "family'": 7499, 'zen': 7500, '52': 7501, 'notebook': 7502, "markle's": 7503, 'captivating': 7504, 'excellence': 7505, '84': 7506, 'colombian': 7507, 'refund': 7508, 'translate': 7509, 'sinks': 7510, 'graders': 7511, 'roth': 7512, 'corps': 7513, '1999': 7514, 'assaulted': 7515, 'alt': 7516, 'misery': 7517, 'dam': 7518, 'courageous': 7519, 'teenagers': 7520, 'lopez': 7521, 'advanced': 7522, 'staten': 7523, 'loneliness': 7524, 'cupboard': 7525, 'respects': 7526, 'bones': 7527, 'accessories': 7528, 'invents': 7529, 'actions': 7530, 'hunters': 7531, "'sex": 7532, 'attended': 7533, 'entrepreneurial': 7534, 'membership': 7535, "house's": 7536, 'berry': 7537, '160': 7538, 'visitors': 7539, 'donna': 7540, 'managed': 7541, 'olsen': 7542, 'disorders': 7543, 'malaysia': 7544, 'result': 7545, 'houseguest': 7546, 'prejudice': 7547, 'squirrel': 7548, 'ranking': 7549, 'homelessness': 7550, 'feminine': 7551, 'unexplained': 7552, 'kourtney': 7553, 'pact': 7554, 'flake': 7555, 'applause': 7556, 'predicted': 7557, 'missiles': 7558, "'making": 7559, 'tpp': 7560, 'textbook': 7561, 'spencer': 7562, 'stake': 7563, 'factor': 7564, 'alec': 7565, 'vicious': 7566, 'melinda': 7567, 'recover': 7568, 'dip': 7569, 'legalizes': 7570, 'purse': 7571, 'rivals': 7572, 'nets': 7573, "park'": 7574, 'thumb': 7575, 'shameless': 7576, 'flew': 7577, 'pockets': 7578, 'bots': 7579, 'eventually': 7580, 'collision': 7581, 'shines': 7582, 'nuts': 7583, 'broth': 7584, 'coke': 7585, 'intense': 7586, 'electronic': 7587, 'raving': 7588, "turkey's": 7589, 'financially': 7590, 'newlyweds': 7591, 'transformation': 7592, "that'll": 7593, 'unsettling': 7594, "journalist's": 7595, 'ominously': 7596, 'beds': 7597, 'clashes': 7598, "obamacare's": 7599, 'specific': 7600, 'speakers': 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'shackled': 12660, 'raping': 12661, "nephew's": 12662, 'heterosexual': 12663, "'wild": 12664, 'wiener': 12665, 'scooter': 12666, 'lollapalooza': 12667, 'outnumber': 12668, 'newsweek': 12669, 'rerun': 12670, 'inflation': 12671, 'treater': 12672, 'established': 12673, 'livestock': 12674, "'beautiful": 12675, "garner's": 12676, 'discipline': 12677, 'equifax': 12678, 'wean': 12679, 'investor': 12680, 'degrasse': 12681, 'landfill': 12682, 'poppy': 12683, 'pacs': 12684, 'scissors': 12685, 'vaxxers': 12686, 'sordid': 12687, 'explicit': 12688, 'stockbroker': 12689, 'aloof': 12690, 'showered': 12691, 'recruitment': 12692, "person's": 12693, 'refreshing': 12694, 'durst': 12695, 'meth': 12696, "'we've": 12697, 'unauthorized': 12698, 'earhart': 12699, 'hemingway': 12700, 'sacred': 12701, 'molesting': 12702, 'psychiatric': 12703, 'roe': 12704, 'moran': 12705, 'masturbation': 12706, 'cannibalism': 12707, 'verify': 12708, 'diarrhea': 12709, 'provoked': 12710, 'avoided': 12711, 'rabbits': 12712, "'moonlight'": 12713, 'chita': 12714, 'racy': 12715, 'skilled': 12716, 'auctioneer': 12717, 'dominate': 12718, 'besieged': 12719, 'daraya': 12720, 'hazard': 12721, 'peasant': 12722, 'migrating': 12723, 'acquittal': 12724, 'jorge': 12725, 'ramos': 12726, 'clinics': 12727, 'halle': 12728, 'glamour': 12729, 'fishes': 12730, 'predatory': 12731, 'steamy': 12732, 'hammering': 12733, 'bigfoot': 12734, 'whitman': 12735, 'chanel': 12736, 'expense': 12737, "museum's": 12738, 'zucker': 12739, 'dread': 12740, "oregon's": 12741, 'jobless': 12742, 'crater': 12743, 'dilapidated': 12744, 'repairs': 12745, 'flashy': 12746, "google's": 12747, 'starter': 12748, 'jackets': 12749, 'satisfied': 12750, 'surfing': 12751, 'commenting': 12752, 'photoshoot': 12753, 'edgar': 12754, 'jogging': 12755, 'dubious': 12756, 'jackie': 12757, 'seals': 12758, 'serbia': 12759, 'mayonnaise': 12760, 'tapped': 12761, 'alma': 12762, 'mater': 12763, 'wilder': 12764, "do'": 12765, 'openings': 12766, "'twas": 12767, 'redesign': 12768, 'alter': 12769, 'hiking': 12770, 'symptom': 12771, 'hulu': 12772, '88': 12773, 'impulses': 12774, 'treatments': 12775, 'runners': 12776, 'functional': 12777, '54': 12778, 'throne': 12779, 'contemporary': 12780, 'tucking': 12781, 'scrolling': 12782, 'sinus': 12783, 'macau': 12784, 'casket': 12785, 'stickers': 12786, 'mercury': 12787, 'mustache': 12788, 'revive': 12789, 'dual': 12790, 'foxconn': 12791, 'walls': 12792, 'quarterback': 12793, 'raccoons': 12794, "'jumpin'": 12795, "flash'": 12796, 'umbrella': 12797, 'bunny': 12798, 'blinds': 12799, "genius'": 12800, 'awakening': 12801, 'unapproved': 12802, 'believing': 12803, "'army": 12804, 'alyssa': 12805, 'dysfunctional': 12806, 'biz': 12807, 'trades': 12808, 'kneel': 12809, 'misconceptions': 12810, 'hebrew': 12811, 'incidents': 12812, 'profanity': 12813, 'deranged': 12814, 'taunting': 12815, 'doubling': 12816, 'opener': 12817, 'lace': 12818, 'pancakes': 12819, 'costumed': 12820, 'deng': 12821, 'xiaoping': 12822, 'eerily': 12823, 'posters': 12824, 'airborne': 12825, 'portman': 12826, "murderer'": 12827, 'folly': 12828, 'naughty': 12829, 'blurb': 12830, 'methadone': 12831, 'overwhelmingly': 12832, 'cheesecake': 12833, 'kimye': 12834, 'schneiderman': 12835, 'omg': 12836, 'childfree': 12837, 'brendan': 12838, 'fraser': 12839, 'portrayed': 12840, 'dilma': 12841, 'rousseff': 12842, "'hidden": 12843, 'octavia': 12844, 'scents': 12845, 'techno': 12846, 'bipartisanship': 12847, 'input': 12848, "'can't": 12849, "wait'": 12850, 'comprehension': 12851, 'quake': 12852, "virginia's": 12853, "'hipster'": 12854, 'taping': 12855, 'daredevil': 12856, 'couches': 12857, "crow's": 12858, "fisherman's": 12859, 'selectively': 12860, "truth'": 12861, 'whiny': 12862, 'noises': 12863, 'raccoon': 12864, 'reincarnation': 12865, 'lighten': 12866, 'ushers': 12867, 'decadent': 12868, 'curtains': 12869, 'icu': 12870, 'flavored': 12871, 'boldly': 12872, 'deserving': 12873, 'evaluating': 12874, 'downed': 12875, 'disputed': 12876, 'irwin': 12877, "light'": 12878, 'cranks': 12879, 'prevail': 12880, 'hopefuls': 12881, 'stripes': 12882, 'surgeries': 12883, 'milking': 12884, 'delivering': 12885, "'rocketman'": 12886, "'troubled'": 12887, 'stacked': 12888, 'boasts': 12889, 'legislator': 12890, 'footprints': 12891, 'panama': 12892, 'surges': 12893, 'medicinal': 12894, 'winnings': 12895, "jupiter's": 12896, "probe's": 12897, "uganda's": 12898, 'detains': 12899, 'footprint': 12900, 'healer': 12901, 'bounds': 12902, "ivanka's": 12903, "'light": 12904, 'degenerates': 12905, "o'rourke": 12906, 'snapping': 12907, 'quinn': 12908, 'lawnmower': 12909, 'immeasurable': 12910, 'meter': 12911, 'narco': 12912, 'hardworking': 12913, 'cherokee': 12914, '15th': 12915, 'blond': 12916, 'clamoring': 12917, 'chaplains': 12918, 'installing': 12919, 'dancer': 12920, 'bubbles': 12921, "'magic": 12922, 'pratt': 12923, 'groot': 12924, 'raw': 12925, 'howie': 12926, "australia's": 12927, 'propublica': 12928, "joe'": 12929, 'reaffirms': 12930, 'beyhive': 12931, 'rachael': 12932, 'warriors': 12933, 'chechen': 12934, 'scarred': 12935, '109': 12936, 'comb': 12937, 'topping': 12938, 'stats': 12939, 'messy': 12940, "'happy": 12941, 'retiree': 12942, "cops'": 12943, 'paraphernalia': 12944, 'combos': 12945, 'sticker': 12946, 'restrictive': 12947, 'tumbling': 12948, 'jepsen': 12949, 'clips': 12950, 'mia': 12951, 'brunt': 12952, 'encouraged': 12953, 'shroud': 12954, 'overestimating': 12955, "eyes'": 12956, 'browns': 12957, 'preps': 12958, 'demonic': 12959, 'schooler': 12960, 'freedoms': 12961, 'graveyard': 12962, "anthony's": 12963, 'blacklivesmatter': 12964, 'erik': 12965, 'picasso': 12966, "sound'": 12967, 'vehemently': 12968, 'distraught': 12969, 'shrunk': 12970, 'awakened': 12971, 'conscience': 12972, 'autoerotic': 12973, 'asphyxiation': 12974, 'surgical': 12975, 'gunmen': 12976, 'gated': 12977, 'savages': 12978, 'legitimately': 12979, 'gravy': 12980, 'overcomes': 12981, 'pharoah': 12982, 'val': 12983, 'chmerkovskiy': 12984, "rihanna's": 12985, 'carts': 12986, 'vu': 12987, 'wigs': 12988, 'adolescent': 12989, 'outlines': 12990, 'fireflies': 12991, 'graduated': 12992, 'sickens': 12993, 'emotion': 12994, "court's": 12995, 'robocall': 12996, 'swiping': 12997, 'charisma': 12998, 'tammy': 12999, 'festivals': 13000, 'illegally': 13001, 'replica': 13002, 'dystopia': 13003, 'surrogates': 13004, 'triumphantly': 13005, 'bitterly': 13006, 'cured': 13007, 'homeowners': 13008, "cia's": 13009, 'honduran': 13010, 'format': 13011, 'celebrations': 13012, 'sully': 13013, 'autopilot': 13014, 'curbing': 13015, 'tapas': 13016, 'snowmobiles': 13017, 'successes': 13018, 'loophole': 13019, "hadn't": 13020, "'thoughts": 13021, "prayers'": 13022, 'collie': 13023, 'zookeeper': 13024, 'bahamas': 13025, "'man": 13026, 'uma': 13027, 'thurman': 13028, "domino's": 13029, 'bakula': 13030, 'gallows': 13031, 'elves': 13032, 'elf': 13033, 'pattinson': 13034, 'chateau': 13035, 'mannequins': 13036, 'reverently': 13037, "sniper'": 13038, 'posed': 13039, 'freezing': 13040, 'gamble': 13041, 'woo': 13042, 'chants': 13043, "greenspan's": 13044, 'attributed': 13045, 'virginity': 13046, 'ignite': 13047, 'unusually': 13048, 'angle': 13049, 'councilman': 13050, "kanye's": 13051, 'mcnugget': 13052, 'tasty': 13053, 'unbelievable': 13054, '20s': 13055, 'peoples': 13056, 'gerrymandered': 13057, "'change'": 13058, 'converted': 13059, 'stiller': 13060, 'peels': 13061, 'employed': 13062, 'appropriate': 13063, 'practitioner': 13064, 'wwi': 13065, 'chatter': 13066, 'sicker': 13067, 'thailand': 13068, 'resurfaces': 13069, 'offerman': 13070, 'twentysomething': 13071, 'disillusionment': 13072, 'buffoon': 13073, 'dedicated': 13074, 'agony': 13075, 'safeguarding': 13076, 'strangulation': 13077, 'clicked': 13078, 'havana': 13079, 'kashmir': 13080, 'boutique': 13081, 'mediating': 13082, 'destructing': 13083, 'surreal': 13084, '140': 13085, 'sunny': 13086, 'establishes': 13087, 'drenched': 13088, "'free": 13089, 'liz': 13090, "may's": 13091, 'monumental': 13092, 'toomey': 13093, 'expansive': 13094, 'gynecologists': 13095, 'starship': 13096, 'madeleine': 13097, 'congratulates': 13098, "players'": 13099, 'superdelegate': 13100, "obamacare'": 13101, 'sludge': 13102, 'fable': 13103, 'wnba': 13104, 'stronger': 13105, 'cruises': 13106, 'wackiest': 13107, 'axis': 13108, 'regretting': 13109, 'doses': 13110, 'greeted': 13111, 'barking': 13112, 'hypnotic': 13113, 'nonetheless': 13114, 'frightening': 13115, "spears'": 13116, 'disoriented': 13117, 'talkative': 13118, 'stained': 13119, 'spaces': 13120, 'whom': 13121, 'inescapable': 13122, 'heather': 13123, 'dipshits': 13124, "own'": 13125, 'sought': 13126, 'spattered': 13127, 'enroll': 13128, 'transitions': 13129, "general's": 13130, 'divine': 13131, 'mercilessly': 13132, 'nutter': 13133, '1992': 13134, 'gerbil': 13135, 'enforce': 13136, 'emergence': 13137, 'nurturing': 13138, 'harlem': 13139, 'gunning': 13140, 'interactions': 13141, 'pivotal': 13142, 'tactical': 13143, 'corpses': 13144, "'lone": 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'marketers': 13202, 'sympathizers': 13203, 'prestigious': 13204, 'timeline': 13205, 'flaws': 13206, 'machiavellian': 13207, 'empowerment': 13208, 'rhode': 13209, 'fec': 13210, 'ravioli': 13211, 'astonishing': 13212, 'booths': 13213, 'deposit': 13214, 'futuristic': 13215, 'clancy': 13216, 'falcons': 13217, 'jennings': 13218, 'unforgivable': 13219, 'mud': 13220, 'halperin': 13221, 'disasters': 13222, "store's": 13223, 'yanks': 13224, 'rebate': 13225, 'mcgregor': 13226, "beast'": 13227, 'squat': 13228, 'misuse': 13229, 'clueless': 13230, 'mudslide': 13231, 'mediocre': 13232, 'goldblum': 13233, 'doppelgänger': 13234, 'rifles': 13235, "'sting'": 13236, 'gentlemen': 13237, 'shells': 13238, "lawrence's": 13239, 'limbaugh': 13240, 'meteorologists': 13241, 'ucla': 13242, 'sensible': 13243, 'walkway': 13244, 'payer': 13245, 'incorrect': 13246, 'jussie': 13247, "justice'": 13248, 'patriotism': 13249, 'loner': 13250, 'incorrectly': 13251, 'rebellion': 13252, 'henderson': 13253, 'consist': 13254, 'downey': 13255, 'hardship': 13256, 'hound': 13257, 'cracking': 13258, 'carnivores': 13259, 'levitt': 13260, 'bystander': 13261, 'rowboat': 13262, 'touring': 13263, 'offbeat': 13264, 'motorized': 13265, 'retiring': 13266, 'procrastinating': 13267, 'contentment': 13268, 'symphony': 13269, 'memoirs': 13270, 'cardiac': 13271, "'great": 13272, 'parrot': 13273, 'licenses': 13274, 'inflatable': 13275, 'fig': 13276, 'defying': 13277, 'pounding': 13278, 'flyer': 13279, "'call": 13280, "baby'": 13281, 'accord': 13282, 'quota': 13283, 'spontaneously': 13284, 'cutler': 13285, 'allergy': 13286, 'stung': 13287, "rio's": 13288, 'compatible': 13289, 'backlogged': 13290, 'vanity': 13291, 'kurt': 13292, 'cheered': 13293, 'institutes': 13294, 'departments': 13295, 'colmes': 13296, 'crossover': 13297, 'pita': 13298, 'marilyn': 13299, 'gunk': 13300, 'expressions': 13301, "'atheist'": 13302, 'blessing': 13303, 'tornadoes': 13304, 'folded': 13305, "nurse's": 13306, 'portrayal': 13307, 'iris': 13308, 'regretted': 13309, 'panty': 13310, "monster'": 13311, 'welding': 13312, 'salon': 13313, 'peta': 13314, 'assigned': 13315, 'recovers': 13316, 'enchants': 13317, 'starlet': 13318, 'heinz': 13319, 'drain': 13320, 'vineyard': 13321, 'azalea': 13322, 'blackouts': 13323, 'pretentious': 13324, 'barbie': 13325, 'kirkman': 13326, 'aereo': 13327, 'jihadist': 13328, 'deciphering': 13329, 'haiku': 13330, 'iv': 13331, 'le': 13332, 'precocious': 13333, "'you've": 13334, "mail'": 13335, 'forgetful': 13336, 'recommended': 13337, 'caloric': 13338, 'intake': 13339, 'displaced': 13340, 'shove': 13341, 'newfront': 13342, 'hickenlooper': 13343, 'dunks': 13344, 'boris': 13345, 'chokes': 13346, "'at": 13347, 'enviros': 13348, 'vie': 13349, 'messed': 13350, 'yearly': 13351, "palin's": 13352, 'pike': 13353, 'eisenberg': 13354, "grader's": 13355, 'markers': 13356, 'relocates': 13357, 'suspending': 13358, "member's": 13359, 'torches': 13360, 'frogs': 13361, 'plagued': 13362, 'yazidi': 13363, 'invading': 13364, 'collude': 13365, 'realities': 13366, '220': 13367, 'fiasco': 13368, 'flourish': 13369, 'veggies': 13370, 'adopt': 13371, 'wheelbarrow': 13372, 'disfigured': 13373, 'tricycle': 13374, 'semi': 13375, 'pringles': 13376, 'eichner': 13377, 'pairing': 13378, 'flattering': 13379, 'closures': 13380, 'suzanne': 13381, 'squeak': 13382, 'meltdown': 13383, 'standout': 13384, "'real'": 13385, 'crashed': 13386, 'boil': 13387, 'robs': 13388, 'bollywood': 13389, 'probing': 13390, 'gmo': 13391, 'downs': 13392, 'vodka': 13393, "putin's": 13394, 'worsen': 13395, 'wsj': 13396, 'markered': 13397, 'initials': 13398, 'participants': 13399, 'suri': 13400, 'redrawing': 13401, 'pornographic': 13402, "'g": 13403, "'coming": 13404, 'assuming': 13405, 'proximity': 13406, 'gotham': 13407, "'dumbing": 13408, 'exercises': 13409, 'swallowing': 13410, 'dragons': 13411, 'guiding': 13412, 'autonomous': 13413, 'discourse': 13414, 'bassist': 13415, "nam's": 13416, 'tr': 13417, 'binder': 13418, 'platter': 13419, 'hush': 13420, 'harpoon': 13421, 'motivation': 13422, 'crossfit': 13423, 'sleepy': 13424, 'baylor': 13425, '270': 13426, 'outcome': 13427, 'legged': 13428, 'formulate': 13429, 'discernible': 13430, 'rattan': 13431, 'ty': 13432, 'herndon': 13433, 'swan': 13434, 'electronics': 13435, "'scandal'": 13436, 'mozzarella': 13437, 'monsters': 13438, 'warwick': 13439, 'creme': 13440, 'retriever': 13441, 'counseling': 13442, "vatican's": 13443, "heaven'": 13444, 'urgent': 13445, 'mecca': 13446, 'wreckage': 13447, "society's": 13448, 'dodge': 13449, 'proudest': 13450, 'leone': 13451, 'urinator': 13452, 'angst': 13453, 'whoopi': 13454, "goodman's": 13455, "manager's": 13456, 'shortcuts': 13457, 'plates': 13458, 'gypsies': 13459, 'blaine': 13460, "public's": 13461, 'charlton': 13462, 'popeyes': 13463, 'biodiversity': 13464, "beethoven's": 13465, 'kickboxing': 13466, 'closeted': 13467, 'reaper': 13468, 'contestant': 13469, 'cummings': 13470, 'entertained': 13471, 'tri': 13472, 'candidacy': 13473, 'emojis': 13474, 'dowd': 13475, 'underground': 13476, 'etsy': 13477, 'belly': 13478, 'slap': 13479, 'redbox': 13480, 'prejudices': 13481, 'manages': 13482, 'sellout': 13483, 'vinci': 13484, 'spam': 13485, 'exonerating': 13486, 'chastises': 13487, 'regulate': 13488, 'hordes': 13489, 'attends': 13490, 'baggage': 13491, 'drowns': 13492, 'accuracy': 13493, 'guitarist': 13494, 'liars': 13495, 'peppered': 13496, 'facilities': 13497, 'regulating': 13498, 'simone': 13499, 'biles': 13500, 'submit': 13501, 'establishing': 13502, 'bares': 13503, 'affirmations': 13504, 'burundi': 13505, "rnc's": 13506, 'hospice': 13507, "'50s": 13508, 'hitman': 13509, 'airways': 13510, 'immoral': 13511, 'goer': 13512, 'omelet': 13513, 'suitable': 13514, 'afi': 13515, 'publishers': 13516, 'innocence': 13517, 'axelrod': 13518, 'seizes': 13519, 'bits': 13520, 'velvet': 13521, 'calorie': 13522, 'sheds': 13523, 'enemy': 13524, "'ghostbusters'": 13525, 'funnies': 13526, 'profound': 13527, 'menendez': 13528, 'primarily': 13529, 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'marchers': 13584, 'pharaoh': 13585, "room'": 13586, 'intimacy': 13587, 'friendships': 13588, 'omar': 13589, 'blagojevich': 13590, "abdul's": 13591, 'audible': 13592, 'scanned': 13593, 'trebek': 13594, 'instinctively': 13595, 'earnest': 13596, 'electricity': 13597, 'rita': 13598, "'historic'": 13599, 'naïve': 13600, 'fathered': 13601, 'columbia': 13602, 'silverman': 13603, 'conceive': 13604, 'thiel': 13605, 'publications': 13606, 'enthusiast': 13607, "patients'": 13608, 'mortician': 13609, 'playfully': 13610, 'spraying': 13611, 'encouragement': 13612, 'personnel': 13613, "'got'": 13614, 'stable': 13615, 'nielsen': 13616, 'detaining': 13617, "'heartbreaking'": 13618, 'showcase': 13619, 'toad': 13620, 'lube': 13621, 'rodgers': 13622, "syria's": 13623, 'mountaintop': 13624, 'unseen': 13625, 'ymca': 13626, 'perot': 13627, 'sentiment': 13628, 'barbados': 13629, 'resorts': 13630, "days'": 13631, 'wits': 13632, 'endgame': 13633, 'disclosed': 13634, 'sundance': 13635, "ali's": 13636, "martin's": 13637, "'smart": 13638, "cookie'": 13639, 'trimester': 13640, 'revitalized': 13641, "'clock": 13642, "raisinets'": 13643, 'coffers': 13644, 'selfless': 13645, 'albuquerque': 13646, 'trails': 13647, 'condé': 13648, 'nast': 13649, 'quelled': 13650, 'contracted': 13651, 'hugged': 13652, 'johansson': 13653, 'bolster': 13654, 'quentin': 13655, 'chops': 13656, 'weds': 13657, 'outstanding': 13658, 'sarkozy': 13659, 'ellie': 13660, 'substance': 13661, 'mating': 13662, "memo'": 13663, "improvement'": 13664, 'ridicule': 13665, 'rejoice': 13666, 'chores': 13667, 'titles': 13668, 'trinity': 13669, 'claritin': 13670, 'contention': 13671, 'crafts': 13672, "'feels": 13673, 'fudge': 13674, "chicken's": 13675, 'edward': 13676, 'snowden': 13677, 'thanked': 13678, 'afghans': 13679, 'insurgents': 13680, 'bestseller': 13681, 'jamal': 13682, 'firefight': 13683, 'teuwen': 13684, 'roasted': 13685, 'rolled': 13686, 'chloe': 13687, 'oversize': 13688, 'displayed': 13689, 'extraction': 13690, 'synthetic': 13691, 'floats': 13692, 'westbrook': 13693, 'buzzer': 13694, 'craziness': 13695, 'wwe': 13696, 'descent': 13697, 'intimidate': 13698, 'helms': 13699, 'infection': 13700, "drama'": 13701, 'wiig': 13702, 'bases': 13703, 'symbolism': 13704, "diet'": 13705, "'ridiculous'": 13706, 'defective': 13707, 'hammered': 13708, 'disrepair': 13709, 'radius': 13710, 'den': 13711, 'sistine': 13712, 'dynamic': 13713, 'toner': 13714, "bears'": 13715, 'strangle': 13716, "'quiet": 13717, "1'": 13718, 'erick': 13719, 'erickson': 13720, "plug'": 13721, "'hope'": 13722, "make'": 13723, 'recreated': 13724, "'moana'": 13725, "hillary's": 13726, "'petty'": 13727, 'cheddar': 13728, 'celebrated': 13729, 'fabulous': 13730, "britain's": 13731, "'go": 13732, 'propecia': 13733, 'shorts': 13734, 'quincy': 13735, 'indictment': 13736, 'oatmeal': 13737, 'recorded': 13738, 'shipping': 13739, 'journals': 13740, "'butt": 13741, 'ar': 13742, 'compensate': 13743, 'gowns': 13744, 'racially': 13745, 'motivated': 13746, 'sorta': 13747, 'escort': 13748, "skin'": 13749, 'illicit': 13750, 'pity': 13751, 'bendy': 13752, 'teasers': 13753, 'highlighting': 13754, "queens'": 13755, 'bridesmaids': 13756, 'submits': 13757, "'unprecedented'": 13758, "lifetime'": 13759, 'breeze': 13760, 'algeria': 13761, 'strangled': 13762, 'weaken': 13763, 'nickelback': 13764, 'reps': 13765, 'taunt': 13766, "working'": 13767, 'ditched': 13768, 'movers': 13769, "stewart's": 13770, 'laborers': 13771, 'prescribe': 13772, 'influencing': 13773, 'sephora': 13774, 'adventurous': 13775, 'flub': 13776, 'epitome': 13777, 'ftc': 13778, 'began': 13779, "iraq's": 13780, 'misogyny': 13781, 'appalled': 13782, 'reconstruct': 13783, "'dawson's": 13784, "creek'": 13785, 'nonstop': 13786, 'detox': 13787, 'rearranging': 13788, 'kitchenaid': 13789, 'mixers': 13790, 'garners': 13791, '240': 13792, 'exceptionally': 13793, 'obvi': 13794, 'shingles': 13795, 'draftkings': 13796, 'actresses': 13797, 'compass': 13798, 'grenade': 13799, 'prized': 13800, 'stallion': 13801, 'veterinarian': 13802, 'hearings': 13803, 'battery': 13804, 'brewers': 13805, 'dion': 13806, "party'": 13807, 'cavs': 13808, 'winds': 13809, 'campbell': 13810, 'tabletop': 13811, 'eileen': 13812, 'disagreements': 13813, 'baptized': 13814, "'99": 13815, 'precedes': 13816, "the'": 13817, 'amphitheater': 13818, 'unsung': 13819, "widow's": 13820, 'outlandish': 13821, 'iger': 13822, 'rickles': 13823, 'constructionist': 13824, "attack'": 13825, 'pursue': 13826, 'administrators': 13827, 'axes': 13828, 'disheveled': 13829, 'approaching': 13830, 'workaholic': 13831, 'unimpressive': 13832, 'treasurer': 13833, 'soaring': 13834, 'demolition': 13835, 'cluster': 13836, 'instagrams': 13837, 'forests': 13838, 'eccentric': 13839, 'gauge': 13840, "paltrow's": 13841, 'attic': 13842, 'restart': 13843, 'fringe': 13844, "'everything's": 13845, 'similar': 13846, "'jurassic": 13847, 'bribery': 13848, 'gloss': 13849, 'knit': 13850, 'meddling': 13851, 'circular': 13852, "'tiny": 13853, 'superpower': 13854, 'whereabouts': 13855, 'rescinds': 13856, 'insulting': 13857, 'sum': 13858, 'velociraptor': 13859, 'soak': 13860, 'villagers': 13861, 'stones': 13862, 'mutants': 13863, 'evolved': 13864, "leader's": 13865, 'bothering': 13866, 'kilborn': 13867, 'visited': 13868, "'baywatch'": 13869, 'screamed': 13870, "'trust": 13871, 'chatting': 13872, 'bankrupting': 13873, 'atonal': 13874, 'thirst': 13875, 'polled': 13876, 'ginger': 13877, 'unlabeled': 13878, 'dana': 13879, 'hader': 13880, 'predicting': 13881, "mo'": 13882, 'archive': 13883, 'jeffrey': 13884, 'maxine': 13885, 'packages': 13886, 'gifs': 13887, 'weathered': 13888, "'follow": 13889, 'gitmo': 13890, 'cholera': 13891, 'incorporate': 13892, 'antibiotic': 13893, 'guided': 13894, 'sleek': 13895, 'devotion': 13896, "'moron'": 13897, 'inhofe': 13898, '115': 13899, 'layout': 13900, 'mashed': 13901, 'motions': 13902, 'rotate': 13903, 'supervisor': 13904, 'nodding': 13905, 'whitey': 13906, 'fragile': 13907, 'reapplies': 13908, 'carmex': 13909, 'intervals': 13910, 'recurring': 13911, 'fetuses': 13912, 'jumper': 13913, 'kotter': 13914, 'clay': 13915, 'branding': 13916, 'unesco': 13917, 'brownback': 13918, 'perjury': 13919, 'uruguay': 13920, 'admire': 13921, 'superintendent': 13922, 'thanking': 13923, 'rockwell': 13924, 'banker': 13925, 'resting': 13926, 'grecian': 13927, 'consensus': 13928, 'proposition': 13929, 'predominantly': 13930, 'oversized': 13931, 'victoria': 13932, 'imported': 13933, "threats'": 13934, "'his": 13935, "action'": 13936, 'puzzles': 13937, 'heston': 13938, 'erect': 13939, 'microphone': 13940, 'haggard': 13941, 'convoy': 13942, 'gory': 13943, 'lunchbox': 13944, "fight'": 13945, 'killers': 13946, 'considerations': 13947, 'balanced': 13948, 'grossed': 13949, 'divorcing': 13950, 'handwriting': 13951, 'pollster': 13952, 'prick': 13953, 'pepperoni': 13954, 'physicist': 13955, 'particle': 13956, 'feign': 13957, 'stride': 13958, 'unconditional': 13959, "'under": 13960, 'electing': 13961, 'gayer': 13962, 'chemicals': 13963, 'freelancers': 13964, 'robe': 13965, 'depicted': 13966, 'beheads': 13967, 'referee': 13968, 'ageism': 13969, 'lego': 13970, "singer's": 13971, 'hospitalized': 13972, 'hostages': 13973, 'wronged': 13974, 'waaah': 13975, 'dishonorable': 13976, 'scripps': 13977, 'appropriations': 13978, 'animation': 13979, 'denuclearization': 13980, 'lean': 13981, "neighborhood's": 13982, 'mortified': 13983, "popeye's": 13984, 'smarter': 13985, 'expels': 13986, 'cons': 13987, 'headset': 13988, 'suburbanite': 13989, 'mumbai': 13990, 'bei': 13991, 'malala': 13992, 'husky': 13993, "'partying'": 13994, 'distractions': 13995, "update'": 13996, 'succumbs': 13997, 'verbal': 13998, 'greeter': 13999, 'ibiza': 14000, 'shaq': 14001, 'preceded': 14002, 'mohamed': 14003, 'tee': 14004, 'unified': 14005, '194': 14006, 'enemies': 14007, 'mcmahon': 14008, 'kattan': 14009, 'incoming': 14010, 'gnar': 14011, 'violated': 14012, 'oats': 14013, 'connect': 14014, 'handsome': 14015, "'okay": 14016, 'shine': 14017, 'usc': 14018, 'admitted': 14019, 'frankenstein': 14020, "couple'": 14021, 'gilbert': 14022, 'associates': 14023, "'your": 14024, 'reformers': 14025, "'any": 14026, 'leelah': 14027, "alcorn's": 14028, 'viable': 14029, 'directs': 14030, 'deliberately': 14031, 'libraries': 14032, "green'": 14033, 'faculty': 14034, 'discontinue': 14035, 'shareholders': 14036, 'denier': 14037, 'conducting': 14038, 'caucuses': 14039, 'compassionate': 14040, 'hologram': 14041, 'vindicates': 14042, "else's": 14043, "giuliani's": 14044, 'damaged': 14045, 'misplaced': 14046, 'decoy': 14047, 'relive': 14048, 'marsh': 14049, 'hopkins': 14050, 'juniors': 14051, 'resisting': 14052, "harper's": 14053, 'trainers': 14054, 'mugshot': 14055, 'caroline': 14056, 'scotch': 14057, 'trolled': 14058, 'delight': 14059, 'bribing': 14060, 'tupperware': 14061, 'crayola': 14062, 'schilling': 14063, 'gape': 14064, 'gibson': 14065, "moderator's": 14066, 'hotness': 14067, 'stepfather': 14068, "meyers'": 14069, 'pedophile': 14070, 'shitting': 14071, 'karaoke': 14072, 'abs': 14073, 'gentrifying': 14074, "apes'": 14075, 'schoolgirls': 14076, "brutal'": 14077, 'anguished': 14078, 'disapproval': 14079, 'wither': 14080, 'meditate': 14081, 'shrinking': 14082, 'flossing': 14083, 'gained': 14084, 'clients': 14085, 'soulful': 14086, 'schemes': 14087, 'mauled': 14088, "'nut": 14089, "'fix": 14090, 'clarinet': 14091, 'hormonal': 14092, 'negotiate': 14093, 'miners': 14094, 'dolby': 14095, 'manhunt': 14096, 'manufacture': 14097, "grandfather's": 14098, 'phillie': 14099, 'phanatic': 14100, 'hannibal': 14101, 'buress': 14102, "shot'": 14103, 'cognitive': 14104, 'sprite': 14105, 'cola': 14106, 'physician': 14107, 'arranged': 14108, 'deconstructing': 14109, 'beekeeper': 14110, 'drains': 14111, 'preservation': 14112, 'demolish': 14113, 'critiquing': 14114, 'abramson': 14115, 'fantasized': 14116, 'congolese': 14117, 'kozlewski': 14118, 'monkfish': 14119, "duterte's": 14120, 'haitian': 14121, 'sincerely': 14122, 'dished': 14123, 'insecurity': 14124, 'inactivity': 14125, 'flopped': 14126, 'pseudonym': 14127, 'supermodel': 14128, 'machinery': 14129, 'bearable': 14130, 'rotary': 14131, 'backwaters': 14132, 'clicks': 14133, 'compound': 14134, 'ronnie': 14135, "'time": 14136, "goodbye'": 14137, "'a'": 14138, 'bhutanese': 14139, 'lhabab': 14140, 'duchen': 14141, 'indirectly': 14142, 'sens': 14143, "wound'": 14144, "'liberal": 14145, "football'": 14146, 'camouflage': 14147, "fuck's": 14148, 'citations': 14149, 'motorists': 14150, "nutcracker'": 14151, 'engulfing': 14152, 'ankara': 14153, "'damn": 14154, 'rosario': 14155, 'educates': 14156, "'rhymezone": 14157, "com'": 14158, 'foreboding': 14159, 'entrees': 14160, 'freud': 14161, 'domhnall': 14162, 'gleeson': 14163, 'hux': 14164, 'audited': 14165, 'systemic': 14166, "'mein": 14167, "führer'": 14168, 'disgrace': 14169, 'sunk': 14170, 'emojisinthewild': 14171, 'umpires': 14172, "'romney'": 14173, 'bowery': 14174, 'hollande': 14175, 'valls': 14176, "moms'": 14177, 'acetaminophen': 14178, 'superrman': 14179, 'copyediting': 14180, 'chipmunks': 14181, "highway's": 14182, 'oblivious': 14183, 'redacted': 14184, 'coed': 14185, 'hyper': 14186, 'neanderthal': 14187, 'bloodlust': 14188, "'piggies'": 14189, 'dangles': 14190, "'clown": 14191, "blackface'": 14192, 'videographer': 14193, 'prefrontal': 14194, 'humanly': 14195, 'ragtag': 14196, 'squealing': 14197, 'umpteenth': 14198, 'hobbit': 14199, 'omaha': 14200, 'selfhood': 14201, 'shoplifters': 14202, 'haitians': 14203, 'ragnarök': 14204, 'port': 14205, 'coe': 14206, "boggs'": 14207, "'sense8'": 14208, 'costliest': 14209, 'effigy': 14210, 'offend': 14211, 'bloodbath': 14212, 'semifinals': 14213, 'skim': 14214, 'flocking': 14215, 'naturist': 14216, 'boner': 14217, 'shipwrecks': 14218, '239': 14219, 'decompression': 14220, 'vernon': 14221, 'quixote': 14222, 'remnick': 14223, 'psychosexual': 14224, 'jebbush': 14225, 'remedies': 14226, 'dusted': 14227, "'conscious": 14228, 'uncoupling': 14229, 'wimping': 14230, 'moaning': 14231, 'stump': 14232, 'tara': 14233, "reid'": 14234, "conan's": 14235, 'disbelief': 14236, 'unrewarded': 14237, 'deserters': 14238, 'dapl': 14239, 'dislike': 14240, 'instructional': 14241, 'coaching': 14242, 'indistinct': 14243, 'hesitates': 14244, 'basset': 14245, 'creepiest': 14246, 'caterers': 14247, 'quarters': 14248, "marry'": 14249, 'swelling': 14250, 'jokester': 14251, 'leotards': 14252, 'peaking': 14253, 'yorkshire': 14254, 'monogrammed': 14255, 'libel': 14256, 'lyne': 14257, 'carrington': 14258, "tales'": 14259, 'bejewel': 14260, "cat's": 14261, 'twinkle': 14262, 'tush': 14263, 'sox': 14264, "'young": 14265, 'dermer': 14266, 'spatula': 14267, 'mounted': 14268, 'bausch': 14269, 'lomb': 14270, 'aviator': 14271, 'contacts': 14272, 'rainbows': 14273, 'butterflies': 14274, "'russiagate'": 14275, 'collapsing': 14276, 'migrated': 14277, 'interplanetary': 14278, 'stab': 14279, 'gibbs': 14280, 'ranted': 14281, 'stabbings': 14282, 'djimon': 14283, 'hounsou': 14284, 'nurture': 14285, 'originate': 14286, 'hurls': 14287, 'detractors': 14288, 'crushworthy': 14289, 'augmented': 14290, 'hutcherson': 14291, "colorado's": 14292, 'statute': 14293, 'attracted': 14294, "d23's": 14295, 'sanderson': 14296, 'felon': 14297, '60s': 14298, 'loads': 14299, 'cower': 14300, "manson's": 14301, 'windfall': 14302, 'haugh': 14303, 'agog': 14304, 'miter': 14305, 'faceshield': 14306, 'comply': 14307, 'magdalene': 14308, 'juicero': 14309, 'juicer': 14310, 'sneakier': 14311, 'boosted': 14312, 'administer': 14313, 'nratv': 14314, "'deranged": 14315, 'vocation': 14316, 'mourdock': 14317, 'weasels': 14318, 'toenail': 14319, 'splurged': 14320, 'unannounced': 14321, 'beverly': 14322, 'ilana': 14323, 'glazer': 14324, 'harassers': 14325, "'lick": 14326, "orphanage's": 14327, 'downgraded': 14328, 'ladybug': 14329, 'rapport': 14330, 'credentials': 14331, "yo'": 14332, 'coped': 14333, 'botany': 14334, 'jalapeño': 14335, "others'": 14336, 'weighed': 14337, "'apes'": 14338, 'threshold': 14339, "jigglypuff's": 14340, 'morgue': 14341, 'intolerant': 14342, 'thoroughly': 14343, 'unimpressed': 14344, 'attacker': 14345, "happen'": 14346, 'macrowave': 14347, 'defrost': 14348, 'spratlys': 14349, 'injustice': 14350, "'hatchet": 14351, 'rooted': 14352, 'visualize': 14353, 'heartbeat': 14354, 'embittered': 14355, 'raisin': 14356, 'heimlich': 14357, 'maneuver': 14358, 'royalties': 14359, 'overtired': 14360, '398': 14361, 'lions': 14362, 'zebras': 14363, 'masse': 14364, 'savanna': 14365, "replace'": 14366, "'moderates'": 14367, 'jonah': 14368, 'neuroscience': 14369, 'falsify': 14370, 'sgt': 14371, 'recaptured': 14372, 'wandering': 14373, "kickin'": 14374, 'wrecks': 14375, 'cayman': 14376, 'ventures': 14377, 'infraction': 14378, 'fervor': 14379, 'capitalists': 14380, 'flusher': 14381, "'hotties'": 14382, 'voluptuous': 14383, 'shapely': 14384, 'clementines': 14385, 'radicalized': 14386, 'recycled': 14387, "acosta's": 14388, 'assertion': 14389, 'ministers': 14390, 'excludes': 14391, 'collisions': 14392, 'residence': 14393, "'and": 14394, 'cocksuckers': 14395, "wednesdays'": 14396, 'thom': 14397, 'yorke': 14398, 'output': 14399, 'upped': 14400, "'clusters'": 14401, 'prowling': 14402, 'forensics': 14403, 'turvy': 14404, 'histories': 14405, 'publicize': 14406, 'yolo': 14407, "linkedin's": 14408, 'albanian': 14409, 'whooped': 14410, 'pressuring': 14411, 'blogging': 14412, 'renaissance': 14413, 'sculptures': 14414, "'anaconda'": 14415, 'mileage': 14416, 'turbans': 14417, 'ragnar': 14418, "'vikings'": 14419, 'cynical': 14420, 'nightmares': 14421, 'rejuvenates': 14422, "grandson's": 14423, 'infallible': 14424, 'weeklong': 14425, "'heartwarming'": 14426, 'xxxxii': 14427, 'macklemore': 14428, "frederick's": 14429, 'anchorage': 14430, 'crotchless': 14431, 'trait': 14432, 'mandarin': 14433, "problems'": 14434, 'literature': 14435, 'chimps': 14436, "'invisible": 14437, 'airwaves': 14438, 'crackle': 14439, 'geddy': 14440, 'headstone': 14441, 'urination': 14442, 'baaaaack': 14443, 'palmolive': 14444, 'systematic': 14445, 'birther': 14446, 'deserter': 14447, 'existential': 14448, 'huddling': 14449, 'scorpions': 14450, 'womb': 14451, 'ja': 14452, "fault'": 14453, "'outlander'": 14454, 'earnhardt': 14455, 'preys': 14456, "'ideas'": 14457, "candy's": 14458, 'aftertaste': 14459, 'nikolai': 14460, 'rimsky': 14461, 'korsakov': 14462, 'inventive': 14463, 'orchestrator': 14464, 'mails': 14465, "offense'": 14466, "orangutan's": 14467, "webpage's": 14468, "'innovate'": 14469, '650': 14470, 'mockument': 14471, 'progressed': 14472, 'juxtaposing': 14473, 'iconography': 14474, '839': 14475, '947': 14476, "'debasement": 14477, "'forrest": 14478, "gump'": 14479, 'automakers': 14480, 'acute': 14481, "'grimsby'": 14482, 'pessimism': 14483, 'stewman': 14484, 'pointy': 14485, 'armstrong': 14486, 'rwandan': 14487, 'podcaster': 14488, 'instinct': 14489, 'disarm': 14490, 'necessity': 14491, 'atrocity': 14492, 'brevity': 14493, 'bratz': 14494, 'rafting': 14495, 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'thaler': 18129, "'engaged'": 18130, 'cunning': 18131, 'subtext': 18132, 'ravens': 18133, 'steelers': 18134, 'canoe': 18135, "'arrogance'": 18136, 'stumbled': 18137, 'subvert': 18138, 'bulletproof': 18139, 'vest': 18140, 'cyst': 18141, 'betty': 18142, 'friedan': 18143, 'garrison': 18144, 'keillor': 18145, 'poached': 18146, 'crosby': 18147, 'dwarven': 18148, 'blacksmith': 18149, 'addcandytoamovie': 18150, "ideas'": 18151, 'wiser': 18152, "'untoward'": 18153, 'subpoenas': 18154, 'cosplay': 18155, 'kingpin': 18156, "o'reilly's": 18157, 'rams': 18158, 'joey': 18159, 'arias': 18160, 'undernutrition': 18161, 'overrun': 18162, 'toads': 18163, 'apollo': 18164, 'kam': 18165, 'tiniest': 18166, 'unrecognizable': 18167, 'zinc': 18168, 'mitty': 18169, "'sad": 18170, "see'": 18171, "glover's": 18172, 'cupid': 18173, 'showrunner': 18174, 'wrest': 18175, 'portraiture': 18176, 'trappings': 18177, 'mick': 18178, "mulvaney's": 18179, 'showcases': 18180, "'glorifying'": 18181, 'comfier': 18182, 'debts': 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time: 34.8 ms (started: 2021-11-21 16:08:59 +00:00)
using gensim library and load the glove embedding. The basic idea behind the GloVe word embedding is to derive the relationship between the words from statistics. Unlike the occurrence matrix, the co-occurrence matrix tells you how often a particular word pair occurs together. Each value in the co-occurrence matrix represents a pair of words occurring together.
#Load Glove model (similar to Word2Vec)
glove_model = api.load('glove-wiki-gigaword-50')
#Size of the model
glove_model.vectors.shape
#Initialize embedding matrix for our dataset with 10000+1 rows (1 for padding word)
#and 50 columns (as embedding size is 50)
embedding_matrix = np.zeros((vocab_size + 1, embedding_dim))
time: 26.8 s (started: 2021-11-21 16:08:59 +00:00)
glove_model['the']
array([ 4.1800e-01, 2.4968e-01, -4.1242e-01, 1.2170e-01, 3.4527e-01,
-4.4457e-02, -4.9688e-01, -1.7862e-01, -6.6023e-04, -6.5660e-01,
2.7843e-01, -1.4767e-01, -5.5677e-01, 1.4658e-01, -9.5095e-03,
1.1658e-02, 1.0204e-01, -1.2792e-01, -8.4430e-01, -1.2181e-01,
-1.6801e-02, -3.3279e-01, -1.5520e-01, -2.3131e-01, -1.9181e-01,
-1.8823e+00, -7.6746e-01, 9.9051e-02, -4.2125e-01, -1.9526e-01,
4.0071e+00, -1.8594e-01, -5.2287e-01, -3.1681e-01, 5.9213e-04,
7.4449e-03, 1.7778e-01, -1.5897e-01, 1.2041e-02, -5.4223e-02,
-2.9871e-01, -1.5749e-01, -3.4758e-01, -4.5637e-02, -4.4251e-01,
1.8785e-01, 2.7849e-03, -1.8411e-01, -1.1514e-01, -7.8581e-01],
dtype=float32)
time: 5.3 ms (started: 2021-11-21 16:09:26 +00:00)
tokenizer_split_data.word_index.items()
dict_items([('<OOV>', 1), ('to', 2), ('of', 3), ('the', 4), ('in', 5), ('for', 6), ('a', 7), ('on', 8), ('and', 9), ('with', 10), ('is', 11), ('new', 12), ('trump', 13), ('man', 14), ('at', 15), ('from', 16), ('about', 17), ('by', 18), ('after', 19), ('you', 20), ('this', 21), ('up', 22), ('out', 23), ('be', 24), ('that', 25), ('as', 26), ('how', 27), ('it', 28), ('not', 29), ('he', 30), ('are', 31), ('just', 32), ('his', 33), ('your', 34), ('what', 35), ('all', 36), ('who', 37), ('has', 38), ('will', 39), ('into', 40), ('report', 41), ('more', 42), ('one', 43), ('have', 44), ('over', 45), ('year', 46), ('u', 47), ('area', 48), ('says', 49), ('why', 50), ('can', 51), ('day', 52), ('s', 53), ('woman', 54), ('like', 55), ('first', 56), ('time', 57), ('donald', 58), ('no', 59), ('get', 60), ('her', 61), ('now', 62), ('old', 63), ('off', 64), ("trump's", 65), ("'", 66), ('people', 67), ('life', 68), ('obama', 69), ('house', 70), ('an', 71), ('white', 72), ('still', 73), ('back', 74), ('make', 75), ('than', 76), ('if', 77), ('was', 78), ('women', 79), ('down', 80), ('when', 81), ('i', 82), ('my', 83), ('clinton', 84), ('could', 85), ('5', 86), ('way', 87), ('they', 88), ('their', 89), ('world', 90), ('before', 91), ('him', 92), ('we', 93), ('would', 94), ('only', 95), ('americans', 96), ('family', 97), ('do', 98), ('gop', 99), ('study', 100), ("it's", 101), ('being', 102), ('black', 103), ('most', 104), ('school', 105), ('3', 106), ('bill', 107), ('so', 108), ('really', 109), ("can't", 110), ('finds', 111), ('years', 112), ('american', 113), ('police', 114), ('best', 115), ('during', 116), ('but', 117), ('watch', 118), ('10', 119), ('she', 120), ('know', 121), ('nation', 122), ('going', 123), ('state', 124), ('death', 125), ("'the", 126), ('video', 127), ('or', 128), ('last', 129), ('should', 130), ('every', 131), ('show', 132), ('president', 133), ('too', 134), ('say', 135), ('home', 136), ('big', 137), ('good', 138), ('campaign', 139), ('things', 140), ('2', 141), ('right', 142), ('against', 143), ('mom', 144), ('health', 145), ('getting', 146), ('000', 147), ('kids', 148), ('hillary', 149), ('self', 150), ('while', 151), ('gets', 152), ("doesn't", 153), ('work', 154), ('party', 155), ('may', 156), ('little', 157), ('where', 158), ('love', 159), ('high', 160), ('need', 161), ('change', 162), ('through', 163), ('some', 164), ('news', 165), ('parents', 166), ('never', 167), ('john', 168), ('take', 169), ('court', 170), ('other', 171), ('own', 172), ("he's", 173), ('stop', 174), ('calls', 175), ('look', 176), ('makes', 177), ('these', 178), ('want', 179), ('election', 180), ('dead', 181), ('child', 182), ('go', 183), ('guy', 184), ('gay', 185), ('them', 186), ('6', 187), ('america', 188), ("don't", 189), ('even', 190), ('local', 191), ('4', 192), ('its', 193), ('takes', 194), ("here's", 195), ('around', 196), ('war', 197), ('plan', 198), ('again', 199), ('bush', 200), ('see', 201), ('real', 202), ('office', 203), ('sex', 204), ('next', 205), ('dad', 206), ('baby', 207), ('finally', 208), ('two', 209), ("nation's", 210), ('our', 211), ('debate', 212), ('7', 213), ('dog', 214), ('1', 215), ('actually', 216), ('got', 217), ('million', 218), ('made', 219), ('much', 220), ('been', 221), ('climate', 222), ('wants', 223), ('week', 224), ('announces', 225), ('sexual', 226), ('another', 227), ('gun', 228), ('reveals', 229), ('long', 230), ('season', 231), ('away', 232), ('college', 233), ('help', 234), ('night', 235), ('senate', 236), ('live', 237), ('enough', 238), ('job', 239), ('shows', 240), ('trying', 241), ('couple', 242), ('food', 243), ('thing', 244), ("won't", 245), ('under', 246), ('give', 247), ('better', 248), ('ever', 249), ('north', 250), ('care', 251), ('there', 252), ('8', 253), ('national', 254), ('facebook', 255), ('us', 256), ('game', 257), ('shooting', 258), ('9', 259), ('without', 260), ('anti', 261), ('body', 262), ('entire', 263), ('money', 264), ('everyone', 265), ('teen', 266), ('already', 267), ('had', 268), ('attack', 269), ('face', 270), ('part', 271), ('media', 272), ('deal', 273), ('york', 274), ("man's", 275), ('top', 276), ('god', 277), ('making', 278), ('tell', 279), ('any', 280), ('story', 281), ('star', 282), ('history', 283), ('supreme', 284), ('congress', 285), ('me', 286), ('paul', 287), ('government', 288), ('having', 289), ('bad', 290), ('children', 291), ('students', 292), ('men', 293), ('line', 294), ('pope', 295), ('movie', 296), ('ways', 297), ('free', 298), ('end', 299), ('releases', 300), ('business', 301), ('tv', 302), ('city', 303), ('fight', 304), ('fire', 305), ('friend', 306), ('does', 307), ('law', 308), ('support', 309), ('must', 310), ('11', 311), ('great', 312), ('son', 313), ('20', 314), ('sanders', 315), ('friends', 316), ('company', 317), ('come', 318), ('public', 319), ('single', 320), ('wedding', 321), ('introduces', 322), ('call', 323), ('former', 324), ('security', 325), ('think', 326), ('morning', 327), ('keep', 328), ('same', 329), ('unveils', 330), ('social', 331), ("didn't", 332), ('find', 333), ('human', 334), ('fans', 335), ('speech', 336), ('voters', 337), ('pretty', 338), ('second', 339), ('girl', 340), ('car', 341), ('presidential', 342), ('admits', 343), ('power', 344), ('thinks', 345), ('full', 346), ('behind', 347), ('film', 348), ('looking', 349), ('name', 350), ('james', 351), ('photos', 352), ('book', 353), ('might', 354), ('scientists', 355), ('once', 356), ('talk', 357), ('republican', 358), ('use', 359), ('democrats', 360), ('found', 361), ('room', 362), ('vote', 363), ('asks', 364), ('win', 365), ('doing', 366), ('because', 367), ('marriage', 368), ('used', 369), ('secret', 370), ('republicans', 371), ('claims', 372), ('group', 373), ('student', 374), ('christmas', 375), ('something', 376), ('fucking', 377), ('twitter', 378), ('middle', 379), ("world's", 380), ('run', 381), ('email', 382), ('case', 383), ('open', 384), ('between', 385), ('control', 386), ('michael', 387), ('future', 388), ('rights', 389), ('violence', 390), ('coming', 391), ('ceo', 392), ('12', 393), ('ban', 394), ('person', 395), ('candidate', 396), ('post', 397), ('photo', 398), ('tax', 399), ('until', 400), ('political', 401), ('2016', 402), ('sure', 403), ('forced', 404), ('always', 405), ('days', 406), ('many', 407), ('minutes', 408), ('goes', 409), ('wife', 410), ('teacher', 411), ('poll', 412), ('plans', 413), ('female', 414), ('killed', 415), ('looks', 416), ('team', 417), ('boy', 418), ('idea', 419), ('warns', 420), ('perfect', 421), ('inside', 422), ('eating', 423), ('wall', 424), ('meet', 425), ('class', 426), ('save', 427), ('month', 428), ('hot', 429), ('each', 430), ('missing', 431), ('past', 432), ('water', 433), ('30', 434), ('running', 435), ('ad', 436), ('start', 437), ('times', 438), ('gives', 439), ('bernie', 440), ('taking', 441), ('breaking', 442), ('everything', 443), ('country', 444), ('put', 445), ('race', 446), ('thousands', 447), ('head', 448), ('let', 449), ('were', 450), ('summer', 451), ("you're", 452), ('comes', 453), ('ryan', 454), ('list', 455), ('george', 456), ('officials', 457), ('hours', 458), ('record', 459), ('yet', 460), ('justice', 461), ('secretary', 462), ('super', 463), ('street', 464), ('father', 465), ('thought', 466), ('cruz', 467), ('art', 468), ('music', 469), ('meeting', 470), ('here', 471), ('heart', 472), ('15', 473), ("'i", 474), ('russia', 475), ('texas', 476), ('place', 477), ('believe', 478), ('left', 479), ('shot', 480), ('talks', 481), ('pay', 482), ('reports', 483), ('iran', 484), ('department', 485), ('judge', 486), ('ready', 487), ('red', 488), ('did', 489), ('phone', 490), ('employee', 491), ('tells', 492), ('mother', 493), ('probably', 494), ('ice', 495), ('living', 496), ('set', 497), ('needs', 498), ('together', 499), ('lives', 500), ('states', 501), ('california', 502), ('chief', 503), ('working', 504), ('personal', 505), ('someone', 506), ('kill', 507), ('nuclear', 508), ('young', 509), ('biden', 510), ('small', 511), ('service', 512), ('prison', 513), ('giving', 514), ('three', 515), ('wrong', 516), ('community', 517), ('hard', 518), ('cancer', 519), ('dies', 520), ('questions', 521), ('wins', 522), ('earth', 523), ('outside', 524), ('tips', 525), ('chris', 526), ('talking', 527), ('word', 528), ('king', 529), ('isis', 530), ('ted', 531), ('korea', 532), ("she's", 533), ('fbi', 534), ('restaurant', 535), ('age', 536), ('town', 537), ('half', 538), ('mike', 539), ('obamacare', 540), ('nothing', 541), ('50', 542), ('very', 543), ('those', 544), ('months', 545), ('few', 546), ('crisis', 547), ('today', 548), ('washington', 549), ('shit', 550), ('military', 551), ('kim', 552), ('administration', 553), ('watching', 554), ('dream', 555), ('store', 556), ('lost', 557), ('tweets', 558), ('owner', 559), ("women's", 560), ('romney', 561), ('feel', 562), ('minute', 563), ('third', 564), ('percent', 565), ('daughter', 566), ('attacks', 567), ('move', 568), ('well', 569), ('problem', 570), ('fan', 571), ("isn't", 572), ('congressman', 573), ('millions', 574), ("i'm", 575), ('letter', 576), ('ex', 577), ('air', 578), ('leaves', 579), ('rock', 580), ('rules', 581), ('since', 582), ('issues', 583), ('following', 584), ('bar', 585), ('francis', 586), ("what's", 587), ('mark', 588), ('girlfriend', 589), ('florida', 590), ('drug', 591), ('break', 592), ('fox', 593), ('trip', 594), ('democratic', 595), ('scott', 596), ('sleep', 597), ('internet', 598), ('hollywood', 599), ('russian', 600), ('muslim', 601), ('leave', 602), ('stephen', 603), ('south', 604), ('beautiful', 605), ('gift', 606), ('less', 607), ('federal', 608), ('latest', 609), ('david', 610), ('wearing', 611), ('cop', 612), ('cat', 613), ('majority', 614), ('straight', 615), ('kind', 616), ('system', 617), ('director', 618), ('fun', 619), ('excited', 620), ('career', 621), ('taylor', 622), ('knows', 623), ('happy', 624), ('called', 625), ('education', 626), ('online', 627), ("obama's", 628), ('response', 629), ('spends', 630), ('union', 631), ('told', 632), ('chinese', 633), ('investigation', 634), ('birthday', 635), ('hit', 636), ('cover', 637), ('feels', 638), ('immigration', 639), ('holiday', 640), ('order', 641), ('favorite', 642), ('100', 643), ('huge', 644), ('leaders', 645), ('offers', 646), ('clearly', 647), ('visit', 648), ('accused', 649), ('hour', 650), ('march', 651), ('key', 652), ('using', 653), ('box', 654), ('lot', 655), ('anything', 656), ('worried', 657), ('abortion', 658), ('birth', 659), ('assault', 660), ('hands', 661), ('different', 662), ('guide', 663), ('kid', 664), ('relationship', 665), ('himself', 666), ('mueller', 667), ("america's", 668), ('t', 669), ('abuse', 670), ('waiting', 671), ('message', 672), ('awards', 673), ('front', 674), ('series', 675), ('thinking', 676), ('rise', 677), ('special', 678), ('far', 679), ('politics', 680), ('protest', 681), ('weekend', 682), ('experts', 683), ('whether', 684), ('buy', 685), ('ask', 686), ('interview', 687), ('mass', 688), ('reasons', 689), ('jr', 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27481), ('quintillion', 27482), ("back'", 27483), ("entertainment's", 27484), ('salke', 27485), ('sherry', 27486), ('lansing', 27487), ("'partner'", 27488), ('desks', 27489), ('terrence', 27490), ('gooding', 27491), ('alignment', 27492), ('071', 27493), ('variables', 27494), ("'gayby'", 27495), ('intergenerational', 27496), ('bewildered', 27497), ('dueling', 27498), ('sanderses', 27499), ('choking\xa0black', 27500), ('shoplifting', 27501), ("'friday", 27502), ("lights'", 27503), ('mountainside', 27504), ('fickle', 27505), ('immense', 27506), ('moby', 27507), ('kaleidoscopic', 27508), ('fruits', 27509), ('senatorial', 27510), ('triumphs', 27511), ('subsidize', 27512), ("table'", 27513), ("'tomb", 27514), ("raider'", 27515), ('enlargement', 27516), ("'schizophrenic'", 27517), ("'bipolar'", 27518), ('churchgoing', 27519), ('widows', 27520), ('goper', 27521), ("'extreme", 27522), ("vetting'", 27523), ('imparting', 27524), ("religions'", 27525), ('strum', 27526), ('swalwell', 27527), ('slathered', 27528), ('directtv', 27529), ('extrovert', 27530), ('astronomical', 27531), ("filter'", 27532), ('curried', 27533), ('odorless', 27534), ('bury', 27535), ("'ravaged'", 27536), ('adjective', 27537), ('chong', 27538), ('weathering', 27539), ("'person", 27540), ('straws', 27541), ("daughter'", 27542), ('ballmer', 27543), ('rebrands', 27544), ("'disappointimals'", 27545), ('unambitious', 27546), ('vetting', 27547), ('altering', 27548), ('aspirations', 27549), ('airs', 27550), ("'rocket", 27551), ('heightened', 27552), ('diplomats', 27553), ("'dissent", 27554), ('objecting', 27555), ('gt', 27556), ('cryptographers', 27557), ("'frpx", 27558), ('k5je', 27559), ('oc4n', 27560), ("e5dn'", 27561), ('gravel', 27562), ('messam', 27563), ('ceremoniously', 27564), ('karma', 27565), ('turban', 27566), ('serif', 27567), ('interminable', 27568), ('repairing', 27569), ('strudel', 27570), ("d'angelo", 27571), ("russell's", 27572), ('benetton', 27573), ('rubble', 27574), ('babysit', 27575), ('rekindle', 27576), ('raffle', 27577), ('disgust', 27578), ("'imminent", 27579), ('flung', 27580), ('almond', 27581), ('packers', 27582), ('freemasons', 27583), ('cambodia', 27584), ("claim'", 27585), ('sweets', 27586), ("'planning", 27587), ('dividing', 27588), ("negro'", 27589), ('wraps', 27590), ('rollerblading', 27591), ("'orcs", 27592), ("'hony'", 27593), ('sauron', 27594), ('adore', 27595), ('defunct', 27596), ('preet', 27597), ('bharara', 27598), ('admitting', 27599), ("laden's", 27600), ('shellie', 27601), ('theroux', 27602), ("'vegetation'", 27603), ('filmgoers', 27604), ("stalin'", 27605), ('tile', 27606), ('backsplash', 27607), ('mewesyria', 27608), ('mediator', 27609), ('qualify', 27610), ('cisgender', 27611), ('masochistic', 27612), ('tankers', 27613), ('enthusiasm', 27614), ('boardwalk', 27615), ('chumps', 27616), ("'kings", 27617), ("'southpaw'", 27618), ('soundtrack', 27619), ('tonto', 27620), ('akon', 27621), ("something'", 27622), ('regressive', 27623), ('metz', 27624), ('timberwolves', 27625), ('observes', 27626), ('impregnable', 27627), ('wozniak', 27628), ('taskforce', 27629), ('mundane', 27630), ('gamed', 27631), ("'which", 27632), ('raphael', 27633), ('ointment', 27634), ('attire', 27635), ('ogre', 27636), ("alpert's", 27637), ("'whipped", 27638), ("delights'", 27639), ('mailed', 27640), ('sprints', 27641), ('gaunt', 27642), ('eqypt', 27643), ('ge', 27644), ('flickering', 27645), ('sanatoriums', 27646), ('birkenstocks', 27647), ("lennon's", 27648), ('mathew', 27649), ('merritt', 27650), ('humorous', 27651), ('washes', 27652), ('jumbled', 27653), ('knievel', 27654), ("generation's", 27655), ('recyclables', 27656), ('nonbinding', 27657), ('numb', 27658), ("'bump", 27659), ("stock'", 27660), ('groggy', 27661), ('mariel', 27662), ("congress'", 27663), ('intricacies', 27664), ('knockoff', 27665), ('internets', 27666), ('watertown', 27667), ('montreal', 27668), ('congregations', 27669), ("existence'", 27670), ('560', 27671), ('scamming', 27672), ('wound', 27673), ('frills', 27674), ('pomp', 27675), ('cartwheel', 27676), ('insta', 27677), ('raciest', 27678), ("google'", 27679), ("'brazen", 27680), ('connector', 27681), ('mensch', 27682), ("crew's", 27683), ('lyons', 27684), ('marcellus', 27685), ('purchaser', 27686), ('connick', 27687), ("'about", 27688), ("buffet's", 27689), ('shrouded', 27690), ('shivers', 27691), ('spine', 27692), ('gorsky', 27693), ('headache', 27694), ('challengers', 27695), ('purported', 27696), ('gi', 27697), ('linger', 27698), ("ballet'", 27699), ('roadtrip', 27700), ('expire', 27701), ('subscribing', 27702), ('buttery', 27703), ('frilly', 27704), ('portraying', 27705), ('hibernating', 27706), ('marathons', 27707), ('levi', 27708), ("celebrity's", 27709), ('fare', 27710), ('lg', 27711), ('hosted', 27712), ('espero', 27713), ('adulterers', 27714), ("liberty'", 27715), ('cobbler', 27716), ('desserts', 27717), ('dime', 27718), ("blankenship's", 27719), ('290', 27720), ('gifting', 27721), ('mcdougal', 27722), ('discussing', 27723), ('connectivity', 27724), ('copenhagen', 27725), ("ukraine's", 27726), ('yatseniuk', 27727), ("'mass", 27728), ("mobs'", 27729), ("detroit's", 27730), ("armstrong's", 27731), ('plugin', 27732), ('stockings', 27733), ('singular', 27734), ('“drain', 27735), ('swamp”', 27736), ('dilute', 27737), ('piracy', 27738), ('cornel', 27739), ("'posed", 27740), ("counterfeit'", 27741), ('automation', 27742), ("'eleanor", 27743), ("rigby'", 27744), ('lynde', 27745), ('smoky', 27746), ('ghanaian', 27747), ('lent', 27748), ('der', 27749), ("beek's", 27750), ('soundandfury', 27751), ('wordpress', 27752), ('kondo', 27753), ('untidy', 27754), ("jews'", 27755), ("'seriously", 27756), ("considering'", 27757), ('itching', 27758), ('pondering', 27759), ("religion's", 27760), ('rudest', 27761), ("'and'", 27762), ('twelve', 27763), ('carnivore', 27764), ('meatheads', 27765), ("'speaking", 27766), ("spanish'", 27767), ('spouses', 27768), ('retreating', 27769), ('archaeologist', 27770)])
time: 75.3 ms (started: 2021-11-21 16:09:26 +00:00)
for word, i in sorted(tokenizer_split_data.word_index.items(),key=lambda x:x[1]):
if i > (vocab_size+1):
break
try:
embedding_vector = glove_model[word] #Reading word's embedding from Glove model for a given word
embedding_matrix[i] = embedding_vector
except:
pass
time: 117 ms (started: 2021-11-21 16:09:26 +00:00)
embedding_matrix
array([[ 0. , 0. , 0. , ..., 0. ,
0. , 0. ],
[ 0. , 0. , 0. , ..., 0. ,
0. , 0. ],
[ 0.68046999, -0.039263 , 0.30186 , ..., -0.073297 ,
-0.064699 , -0.26043999],
...,
[ 0. , 0. , 0. , ..., 0. ,
0. , 0. ],
[ 0. , 0. , 0. , ..., 0. ,
0. , 0. ],
[ 0. , 0. , 0. , ..., 0. ,
0. , 0. ]])
time: 4.6 ms (started: 2021-11-21 16:09:26 +00:00)
( Fit the model and check the validation accuracy)
model1_glove_emdb = tf.keras.Sequential()
model1_glove_emdb.add(tf.keras.layers.Embedding(vocab_size + 1, #Vocablury size
embedding_dim, #Embedding size
weights=[embedding_matrix],
trainable=False,
input_length=max_length) #Number of words in each review
)
model1_glove_emdb.output
<KerasTensor: shape=(None, 100, 50) dtype=float32 (created by layer 'embedding_1')>
time: 25.2 ms (started: 2021-11-21 16:09:26 +00:00)
model1_glove_emdb.add(tf.keras.layers.Dropout(0.30))
model1_glove_emdb.add(tf.keras.layers.Bidirectional(tf.keras.layers.LSTM(200, return_sequences = True),merge_mode='sum')) #RNN State - size of cell state and hidden state
model1_glove_emdb.output
<KerasTensor: shape=(None, 100, 200) dtype=float32 (created by layer 'bidirectional')>
time: 672 ms (started: 2021-11-21 16:09:26 +00:00)
model1_glove_emdb.add(tf.keras.layers.Dropout(0.20))
model1_glove_emdb.add(tf.keras.layers.Bidirectional(tf.keras.layers.LSTM(150, return_sequences = True),merge_mode='concat')) #RNN State - size of cell state and hidden state
model1_glove_emdb.output
<KerasTensor: shape=(None, 100, 300) dtype=float32 (created by layer 'bidirectional_1')>
time: 626 ms (started: 2021-11-21 16:09:26 +00:00)
model1_glove_emdb.add(tf.keras.layers.Dropout(0.20))
model1_glove_emdb.add(tf.keras.layers.Bidirectional(tf.keras.layers.LSTM(100), merge_mode='mul')) #RNN State - size of cell state and hidden state
model1_glove_emdb.output
<KerasTensor: shape=(None, 100) dtype=float32 (created by layer 'bidirectional_2')>
time: 602 ms (started: 2021-11-21 16:09:27 +00:00)
model1_glove_emdb.add(tf.keras.layers.Dropout(0.20))
model1_glove_emdb.add(tf.keras.layers.Dense(1,activation='sigmoid'))
model1_glove_emdb.summary()
Model: "sequential_1"
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
embedding_1 (Embedding) (None, 100, 50) 1500050
dropout_1 (Dropout) (None, 100, 50) 0
bidirectional (Bidirectiona (None, 100, 200) 401600
l)
dropout_2 (Dropout) (None, 100, 200) 0
bidirectional_1 (Bidirectio (None, 100, 300) 421200
nal)
dropout_3 (Dropout) (None, 100, 300) 0
bidirectional_2 (Bidirectio (None, 100) 320800
nal)
dropout_4 (Dropout) (None, 100) 0
dense_1 (Dense) (None, 1) 101
=================================================================
Total params: 2,643,751
Trainable params: 1,143,701
Non-trainable params: 1,500,050
_________________________________________________________________
time: 21.2 ms (started: 2021-11-21 16:09:28 +00:00)
#Compile the model
model1_glove_emdb.compile(optimizer='adam',loss='binary_crossentropy',metrics=['accuracy'])
time: 12.1 ms (started: 2021-11-21 16:09:28 +00:00)
# model_n.summary()
history_model1 = model1_glove_emdb.fit(train_padded_split_data, y_train,
epochs=10,
batch_size=32, validation_data=(test_padded_split_data, y_test))
Epoch 1/10 716/716 [==============================] - 47s 52ms/step - loss: 0.5776 - accuracy: 0.7001 - val_loss: 0.4760 - val_accuracy: 0.7748 Epoch 2/10 716/716 [==============================] - 35s 48ms/step - loss: 0.4724 - accuracy: 0.7750 - val_loss: 0.3958 - val_accuracy: 0.8188 Epoch 3/10 716/716 [==============================] - 35s 49ms/step - loss: 0.4291 - accuracy: 0.8006 - val_loss: 0.3778 - val_accuracy: 0.8312 Epoch 4/10 716/716 [==============================] - 35s 49ms/step - loss: 0.3984 - accuracy: 0.8197 - val_loss: 0.3742 - val_accuracy: 0.8311 Epoch 5/10 716/716 [==============================] - 36s 50ms/step - loss: 0.3754 - accuracy: 0.8316 - val_loss: 0.3454 - val_accuracy: 0.8461 Epoch 6/10 716/716 [==============================] - 36s 50ms/step - loss: 0.3501 - accuracy: 0.8426 - val_loss: 0.3297 - val_accuracy: 0.8519 Epoch 7/10 716/716 [==============================] - 36s 50ms/step - loss: 0.3325 - accuracy: 0.8535 - val_loss: 0.3435 - val_accuracy: 0.8482 Epoch 8/10 716/716 [==============================] - 36s 50ms/step - loss: 0.3199 - accuracy: 0.8581 - val_loss: 0.3226 - val_accuracy: 0.8573 Epoch 9/10 716/716 [==============================] - 36s 50ms/step - loss: 0.3012 - accuracy: 0.8687 - val_loss: 0.3236 - val_accuracy: 0.8594 Epoch 10/10 716/716 [==============================] - 36s 50ms/step - loss: 0.2860 - accuracy: 0.8770 - val_loss: 0.3181 - val_accuracy: 0.8632 time: 6min 7s (started: 2021-11-21 16:09:28 +00:00)
plt.plot(history_model1.history['accuracy'])
plt.plot(history_model1.history['val_accuracy'])
plt.title('model accuracy')
plt.ylabel('accuracy')
plt.xlabel('epoch')
plt.legend(['train', 'validation'], loc='upper left')
plt.show()
time: 327 ms (started: 2021-11-21 16:15:35 +00:00)
Train and validation accuracy is close to each other and there are not a lot of divergance observed in the graph. This model looks like a good fit model and there is very less overfit observed in the model.
# summarize history for loss
plt.plot(history_model1.history['loss'])
plt.plot(history_model1.history['val_loss'])
plt.title('model loss')
plt.ylabel('loss')
plt.xlabel('epoch')
plt.legend(['train', 'test'], loc='upper left')
plt.show()
time: 323 ms (started: 2021-11-21 16:15:35 +00:00)
Train and validation loss is close to each other and there are not a lot of divergance observed in the graph. This model looks like a good fit model and there is very less overfit observed in the model.
To build this model we are using multiple layers of bidirectional LSTM and each with different hidden and cell state size and with few dropput layers. One thing we need to observe here is that using drop out layer right before sigmoid. This has helped to reduce overfit of the model. This is bit unconventional, since we seldom used dropout layer just above sigmoid output layer. But in this case it has helped to reduce the overfitting of the model.
We are using glove embedding for weights and this has helped to greatly increase accuracy of the model. This has also helped to reduce training time for this model.
model2_glove_emdb = tf.keras.Sequential()
model2_glove_emdb.add(tf.keras.layers.Embedding(vocab_size + 1, #Vocablury size
embedding_dim, #Embedding size
weights=[embedding_matrix],
trainable=False,
input_length=max_length) #Number of words in each review
)
model2_glove_emdb.output
<KerasTensor: shape=(None, 100, 50) dtype=float32 (created by layer 'embedding_2')>
time: 23.8 ms (started: 2021-11-21 16:15:35 +00:00)
model2_glove_emdb.add(tf.keras.layers.Dropout(0.15))
model2_glove_emdb.add(tf.keras.layers.Bidirectional(tf.keras.layers.LSTM(200))) #RNN State - size of cell state and hidden state
model2_glove_emdb.output
<KerasTensor: shape=(None, 400) dtype=float32 (created by layer 'bidirectional_3')>
time: 626 ms (started: 2021-11-21 16:15:36 +00:00)
model2_glove_emdb.add(tf.keras.layers.Dropout(0.15))
model2_glove_emdb.add(tf.keras.layers.Dense(1,activation='sigmoid'))
model2_glove_emdb.summary()
Model: "sequential_2"
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
embedding_2 (Embedding) (None, 100, 50) 1500050
dropout_5 (Dropout) (None, 100, 50) 0
bidirectional_3 (Bidirectio (None, 400) 401600
nal)
dropout_6 (Dropout) (None, 400) 0
dense_2 (Dense) (None, 1) 401
=================================================================
Total params: 1,902,051
Trainable params: 402,001
Non-trainable params: 1,500,050
_________________________________________________________________
time: 23.6 ms (started: 2021-11-21 16:15:36 +00:00)
#Compile the model
model2_glove_emdb.compile(optimizer='adam',loss='binary_crossentropy',metrics=['accuracy'])
time: 12.2 ms (started: 2021-11-21 16:15:36 +00:00)
# model_n.summary()
history_model2 = model2_glove_emdb.fit(train_padded_split_data, y_train,
epochs=10,
batch_size=32, validation_data=(test_padded_split_data, y_test))
Epoch 1/10 716/716 [==============================] - 18s 20ms/step - loss: 0.5486 - accuracy: 0.7215 - val_loss: 0.4542 - val_accuracy: 0.7848 Epoch 2/10 716/716 [==============================] - 13s 18ms/step - loss: 0.4392 - accuracy: 0.7994 - val_loss: 0.3924 - val_accuracy: 0.8171 Epoch 3/10 716/716 [==============================] - 13s 18ms/step - loss: 0.4549 - accuracy: 0.7830 - val_loss: 0.6062 - val_accuracy: 0.6716 Epoch 4/10 716/716 [==============================] - 13s 18ms/step - loss: 0.4190 - accuracy: 0.8062 - val_loss: 0.3626 - val_accuracy: 0.8332 Epoch 5/10 716/716 [==============================] - 13s 18ms/step - loss: 0.3474 - accuracy: 0.8465 - val_loss: 0.3866 - val_accuracy: 0.8215 Epoch 6/10 716/716 [==============================] - 13s 18ms/step - loss: 0.3217 - accuracy: 0.8608 - val_loss: 0.3450 - val_accuracy: 0.8512 Epoch 7/10 716/716 [==============================] - 13s 18ms/step - loss: 0.3113 - accuracy: 0.8652 - val_loss: 0.3445 - val_accuracy: 0.8482 Epoch 8/10 716/716 [==============================] - 13s 18ms/step - loss: 0.2859 - accuracy: 0.8755 - val_loss: 0.3306 - val_accuracy: 0.8555 Epoch 9/10 716/716 [==============================] - 13s 18ms/step - loss: 0.2623 - accuracy: 0.8878 - val_loss: 0.3223 - val_accuracy: 0.8555 Epoch 10/10 716/716 [==============================] - 13s 18ms/step - loss: 0.2456 - accuracy: 0.8975 - val_loss: 0.3210 - val_accuracy: 0.8650 time: 2min 25s (started: 2021-11-21 16:15:36 +00:00)
plt.plot(history_model2.history['accuracy'])
plt.plot(history_model2.history['val_accuracy'])
plt.title('model accuracy')
plt.ylabel('accuracy')
plt.xlabel('epoch')
plt.legend(['train', 'validation'], loc='upper left')
plt.show()
time: 302 ms (started: 2021-11-21 16:18:02 +00:00)
# summarize history for loss
plt.plot(history_model2.history['loss'])
plt.plot(history_model2.history['val_loss'])
plt.title('model loss')
plt.ylabel('loss')
plt.xlabel('epoch')
plt.legend(['train', 'test'], loc='upper left')
plt.show()
time: 309 ms (started: 2021-11-21 16:18:02 +00:00)
It looks like this model and the one earlier are having same kind of behaviour
embedding_matrix_whole_dataset = np.zeros((vocab_size + 1, embedding_dim))
time: 7.83 ms (started: 2021-11-21 16:18:02 +00:00)
for word, i in sorted(tokenizer_whole_dataset.word_index.items(),key=lambda x:x[1]):
if i > (vocab_size+1):
break
try:
embedding_vector = glove_model[word] #Reading word's embedding from Glove model for a given word
embedding_matrix_whole_dataset[i] = embedding_vector
except:
pass
time: 101 ms (started: 2021-11-21 16:18:02 +00:00)
model3_glove_emdb_wholeDataset = tf.keras.Sequential()
model3_glove_emdb_wholeDataset.add(tf.keras.layers.Embedding(vocab_size + 1, #Vocablury size
embedding_dim, #Embedding size
weights=[embedding_matrix_whole_dataset],
trainable=False,
input_length=max_length) #Number of words in each review
)
model3_glove_emdb_wholeDataset.output
<KerasTensor: shape=(None, 100, 50) dtype=float32 (created by layer 'embedding_3')>
time: 22.3 ms (started: 2021-11-21 16:18:02 +00:00)
In bi-directional LSTM model,
1) data from one LSTM gets fed in forward direction
2) While for the other LSTM, data is fed in reverse direction.
The arrangement will aid for better learning of sentences.
Output of LSTM will get Combined. There are different merge mode available (Add, Concat, mul)
The words that come in the sentences can change the meaning and it is recommended to use it.
model3_glove_emdb_wholeDataset.add(tf.keras.layers.Dropout(0.15))
model3_glove_emdb_wholeDataset.add(tf.keras.layers.Bidirectional(tf.keras.layers.LSTM(200, return_sequences = True),merge_mode='sum')) #RNN State - size of cell state and hidden state
model3_glove_emdb_wholeDataset.output
<KerasTensor: shape=(None, 100, 200) dtype=float32 (created by layer 'bidirectional_4')>
time: 625 ms (started: 2021-11-21 16:18:02 +00:00)
model3_glove_emdb_wholeDataset.add(tf.keras.layers.Dropout(0.10))
model3_glove_emdb_wholeDataset.add(tf.keras.layers.Bidirectional(tf.keras.layers.LSTM(150, return_sequences = True),merge_mode='concat')) #RNN State - size of cell state and hidden state
model3_glove_emdb_wholeDataset.output
<KerasTensor: shape=(None, 100, 300) dtype=float32 (created by layer 'bidirectional_5')>
time: 610 ms (started: 2021-11-21 16:18:03 +00:00)
model3_glove_emdb_wholeDataset.add(tf.keras.layers.Bidirectional(tf.keras.layers.LSTM(100), merge_mode='mul')) #RNN State - size of cell state and hidden state
model3_glove_emdb_wholeDataset.output
<KerasTensor: shape=(None, 100) dtype=float32 (created by layer 'bidirectional_6')>
time: 597 ms (started: 2021-11-21 16:18:04 +00:00)
model3_glove_emdb_wholeDataset.add(tf.keras.layers.Dropout(0.15))
model3_glove_emdb_wholeDataset.add(tf.keras.layers.Dense(1,activation='sigmoid'))
model3_glove_emdb_wholeDataset.summary()
Model: "sequential_3"
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
embedding_3 (Embedding) (None, 100, 50) 1500050
dropout_7 (Dropout) (None, 100, 50) 0
bidirectional_4 (Bidirectio (None, 100, 200) 401600
nal)
dropout_8 (Dropout) (None, 100, 200) 0
bidirectional_5 (Bidirectio (None, 100, 300) 421200
nal)
bidirectional_6 (Bidirectio (None, 100) 320800
nal)
dropout_9 (Dropout) (None, 100) 0
dense_3 (Dense) (None, 1) 101
=================================================================
Total params: 2,643,751
Trainable params: 1,143,701
Non-trainable params: 1,500,050
_________________________________________________________________
time: 25.8 ms (started: 2021-11-21 16:18:04 +00:00)
#Compile the model
model3_glove_emdb_wholeDataset.compile(optimizer='adam',loss='binary_crossentropy',metrics=['accuracy'])
time: 8.16 ms (started: 2021-11-21 16:18:04 +00:00)
# model_n.summary()
history_model3_glove_emdb_wholeDataset = model3_glove_emdb_wholeDataset.fit(train_padded_split_data, y_train,
epochs=10,
batch_size=32, validation_data=(test_padded_split_data, y_test))
Epoch 1/10 716/716 [==============================] - 46s 51ms/step - loss: 0.6497 - accuracy: 0.6136 - val_loss: 0.5933 - val_accuracy: 0.6571 Epoch 2/10 716/716 [==============================] - 34s 48ms/step - loss: 0.5724 - accuracy: 0.6853 - val_loss: 0.5386 - val_accuracy: 0.7154 Epoch 3/10 716/716 [==============================] - 34s 48ms/step - loss: 0.5239 - accuracy: 0.7282 - val_loss: 0.5263 - val_accuracy: 0.7379 Epoch 4/10 716/716 [==============================] - 34s 47ms/step - loss: 0.4885 - accuracy: 0.7538 - val_loss: 0.4827 - val_accuracy: 0.7589 Epoch 5/10 716/716 [==============================] - 35s 49ms/step - loss: 0.4603 - accuracy: 0.7725 - val_loss: 0.4605 - val_accuracy: 0.7755 Epoch 6/10 716/716 [==============================] - 35s 49ms/step - loss: 0.4247 - accuracy: 0.7999 - val_loss: 0.4643 - val_accuracy: 0.7783 Epoch 7/10 716/716 [==============================] - 35s 49ms/step - loss: 0.3923 - accuracy: 0.8145 - val_loss: 0.4592 - val_accuracy: 0.7736 Epoch 8/10 716/716 [==============================] - 35s 49ms/step - loss: 0.3581 - accuracy: 0.8376 - val_loss: 0.4552 - val_accuracy: 0.7839 Epoch 9/10 716/716 [==============================] - 35s 50ms/step - loss: 0.3214 - accuracy: 0.8561 - val_loss: 0.4668 - val_accuracy: 0.7890 Epoch 10/10 716/716 [==============================] - 35s 49ms/step - loss: 0.2876 - accuracy: 0.8769 - val_loss: 0.4970 - val_accuracy: 0.7921 time: 6min (started: 2021-11-21 16:18:04 +00:00)
plt.plot(history_model3_glove_emdb_wholeDataset.history['accuracy'])
plt.plot(history_model3_glove_emdb_wholeDataset.history['val_accuracy'])
plt.title('model accuracy')
plt.ylabel('accuracy')
plt.xlabel('epoch')
plt.legend(['train', 'validation'], loc='upper left')
plt.show()
time: 324 ms (started: 2021-11-21 16:24:05 +00:00)
# summarize history for loss
plt.plot(history_model3_glove_emdb_wholeDataset.history['loss'])
plt.plot(history_model3_glove_emdb_wholeDataset.history['val_loss'])
plt.title('model loss')
plt.ylabel('loss')
plt.xlabel('epoch')
plt.legend(['train', 'test'], loc='upper left')
plt.show()
time: 319 ms (started: 2021-11-21 16:24:05 +00:00)
There is a hige gap in the loss and bit less accurate than other model and hence cannot be used for prediction
model4_glove_emdb_wholeDataset = tf.keras.Sequential()
model4_glove_emdb_wholeDataset.add(tf.keras.layers.Embedding(vocab_size + 1, #Vocablury size
embedding_dim, #Embedding size
weights=[embedding_matrix_whole_dataset],
trainable=False,
input_length=max_length) #Number of words in each review
)
model4_glove_emdb_wholeDataset.output
<KerasTensor: shape=(None, 100, 50) dtype=float32 (created by layer 'embedding_4')>
time: 23.2 ms (started: 2021-11-21 16:24:06 +00:00)
# model4_glove_emdb_wholeDataset.add(tf.keras.layers.Dropout(0.15))
model4_glove_emdb_wholeDataset.add(tf.keras.layers.Bidirectional(tf.keras.layers.LSTM(200)))#,merge_mode='sum') #RNN State - size of cell state and hidden state
model4_glove_emdb_wholeDataset.output
<KerasTensor: shape=(None, 400) dtype=float32 (created by layer 'bidirectional_7')>
time: 628 ms (started: 2021-11-21 16:24:06 +00:00)
model4_glove_emdb_wholeDataset.add(tf.keras.layers.Dense(1,activation='sigmoid'))
model4_glove_emdb_wholeDataset.output
<KerasTensor: shape=(None, 1) dtype=float32 (created by layer 'dense_4')>
time: 18.3 ms (started: 2021-11-21 16:24:06 +00:00)
model4_glove_emdb_wholeDataset.summary()
Model: "sequential_4"
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
embedding_4 (Embedding) (None, 100, 50) 1500050
bidirectional_7 (Bidirectio (None, 400) 401600
nal)
dense_4 (Dense) (None, 1) 401
=================================================================
Total params: 1,902,051
Trainable params: 402,001
Non-trainable params: 1,500,050
_________________________________________________________________
time: 2.44 ms (started: 2021-11-21 16:24:06 +00:00)
#Compile the model
model4_glove_emdb_wholeDataset.compile(optimizer='adam',loss='binary_crossentropy',metrics=['accuracy'])
time: 6.54 ms (started: 2021-11-21 16:24:06 +00:00)
# model_n.summary()
history_model4_wholeDataset = model4_glove_emdb_wholeDataset.fit(train_whole_dataset, y_train,
epochs=10,
batch_size=32, validation_data=(validation_whole_dataset, y_test))
Epoch 1/10 716/716 [==============================] - 17s 19ms/step - loss: 0.5295 - accuracy: 0.7359 - val_loss: 0.4320 - val_accuracy: 0.7952 Epoch 2/10 716/716 [==============================] - 13s 18ms/step - loss: 0.3933 - accuracy: 0.8225 - val_loss: 0.3666 - val_accuracy: 0.8382 Epoch 3/10 716/716 [==============================] - 13s 18ms/step - loss: 0.3360 - accuracy: 0.8501 - val_loss: 0.3420 - val_accuracy: 0.8484 Epoch 4/10 716/716 [==============================] - 13s 18ms/step - loss: 0.2949 - accuracy: 0.8713 - val_loss: 0.3304 - val_accuracy: 0.8501 Epoch 5/10 716/716 [==============================] - 13s 18ms/step - loss: 0.2533 - accuracy: 0.8912 - val_loss: 0.3391 - val_accuracy: 0.8557 Epoch 6/10 716/716 [==============================] - 13s 18ms/step - loss: 0.2140 - accuracy: 0.9126 - val_loss: 0.3355 - val_accuracy: 0.8583 Epoch 7/10 716/716 [==============================] - 13s 18ms/step - loss: 0.1719 - accuracy: 0.9309 - val_loss: 0.3576 - val_accuracy: 0.8519 Epoch 8/10 716/716 [==============================] - 13s 18ms/step - loss: 0.1298 - accuracy: 0.9499 - val_loss: 0.4127 - val_accuracy: 0.8491 Epoch 9/10 716/716 [==============================] - 13s 18ms/step - loss: 0.0940 - accuracy: 0.9650 - val_loss: 0.4295 - val_accuracy: 0.8557 Epoch 10/10 716/716 [==============================] - 13s 18ms/step - loss: 0.0658 - accuracy: 0.9765 - val_loss: 0.4828 - val_accuracy: 0.8508 time: 2min 11s (started: 2021-11-21 16:24:06 +00:00)
plt.plot(history_model4_wholeDataset.history['accuracy'])
plt.plot(history_model4_wholeDataset.history['val_accuracy'])
plt.title('model accuracy')
plt.ylabel('accuracy')
plt.xlabel('epoch')
plt.legend(['train', 'validation'], loc='upper left')
plt.show()
time: 318 ms (started: 2021-11-21 16:26:18 +00:00)
# summarize history for loss
plt.plot(history_model4_wholeDataset.history['loss'])
plt.plot(history_model4_wholeDataset.history['val_loss'])
plt.title('model loss')
plt.ylabel('loss')
plt.xlabel('epoch')
plt.legend(['train', 'test'], loc='upper left')
plt.show()
time: 320 ms (started: 2021-11-21 16:26:18 +00:00)
There is a hige gap in the accuracy and loss graph, thsi model looks like a overfit model and hence cannot be used for prediction
import seaborn as sns
from sklearn.metrics import confusion_matrix
import matplotlib.pyplot as plt
def print_confusion_matrix(testY, predicted_test_y):
mat = confusion_matrix(testY, predicted_test_y)
plt.figure(figsize=(4, 4))
sns.set()
sns.heatmap(mat.T, square=True, annot=True, fmt='d', cbar=False,
xticklabels=np.unique(testY),
yticklabels=np.unique(testY))
plt.xlabel('true label')
plt.ylabel('predicted label')
plt.show()
time: 6.46 ms (started: 2021-11-21 16:26:19 +00:00)
#Calculate accuracy on Unseen Dataset
sarcasm_test = model1_glove_emdb.predict(test_padded_split_data)
sarcasm_predicted_test_y_binary = sarcasm_test >= 0.5
print_confusion_matrix(y_test, sarcasm_predicted_test_y_binary)
# # model_n.summary()
# history_model2 = model2_glove_emdb.fit(train_padded_split_data, y_train,
# epochs=10,
# batch_size=32, validation_data=(test_padded_split_data, y_test))
time: 5.44 s (started: 2021-11-21 16:26:19 +00:00)
Observation from above graphs are mentioned below
1) There are 2658 True negative : True negatives (TN): We predicted no, and they are negative sentiment from viewers of movie.
2) There are 2257 True positive. True positives (TP): These are cases in which we predicted yes (they are positive sentiment about the movie), and they are positive sentiment.
3) There are 501 False positive : false positives (FP): We predicted yes, but that particular review was negative. (Also known as a "Type I error.")
4) There are 282 False negative : false negatives (FN): We predicted no, but that particular review was Positive. (Also known as a "Type II error.")
import pandas as pd
df_sarcasm_compare_results = pd.DataFrame()
df_sarcasm_compare_results['Actual_Data'] = np.where(y_test == 1, 'Sarcastic','Not a Sarcastic')
df_sarcasm_compare_results['Predicted_Data']= np.where(sarcasm_predicted_test_y_binary, 'Sarcastic','Not a Sarcastic')
time: 10.3 ms (started: 2021-11-21 16:26:24 +00:00)
df_sarcasm_compare_results
| Actual_Data | Predicted_Data | |
|---|---|---|
| 0 | Not a Sarcastic | Not a Sarcastic |
| 1 | Sarcastic | Sarcastic |
| 2 | Sarcastic | Sarcastic |
| 3 | Sarcastic | Sarcastic |
| 4 | Not a Sarcastic | Not a Sarcastic |
| ... | ... | ... |
| 5719 | Not a Sarcastic | Not a Sarcastic |
| 5720 | Sarcastic | Not a Sarcastic |
| 5721 | Not a Sarcastic | Not a Sarcastic |
| 5722 | Not a Sarcastic | Not a Sarcastic |
| 5723 | Sarcastic | Sarcastic |
5724 rows × 2 columns
time: 16.4 ms (started: 2021-11-21 16:26:24 +00:00)
In the given project objective is to determine if the headline is sarcastic or not. For predicting same we have used bidirectional LSTM models. We have evaluated four models. Two model are fed data where in entire dataset was tokenised at once, where as other two models are fed with data after spliting the input data into train and test split and then tokenising them. The ideas here is to mask the data altogether as a validation data and then provide it to the model for validation purpose. This method has worked out good and the accuracy of the model observed is also good.
In this project we are using glove embedding, by this way we are transfer learning and reducing the number of weights and biases which has to be learnt and thus reducing the resource and execution time and increasing the accuracy of the model. Transfer learning is a machine learning method where a model developed for a task is reused as the starting point for a model on a second task. It is a popular approach in deep learning where pre-trained models are used as the starting point on natural language processing tasks given the vast compute and time resources required to develop neural network models on these problems and from the huge jumps in skill that they provide on related problems. Compared to word2vec, GloVe allows for parallel implementation, which means that it's easier to train over more data. It is believed (GloVe) to combine the benefits of the word2vec skip-gram model in the word analogy tasks, with those of matrix factorization methods exploiting global statistical information.
We have evaluated four model, among them the model #1 which is having multiple bidirectional LSTM layers and which is trained on the data which is split tokenised has worked well and for the same model the accuracy score are mentioned in above cells. The accuracy of model when compared with train and validation it looks like the model is a good fit and there is very less overfit observed in the model. We are using glove embedding for weights and this has helped to greatly increase accuracy of the model. This has also helped to reduce training time for this model. Model #1 and model#2 seems to be performing in same way but the validation and observation on these two model can be ascertained only upon training with more data. Model #1 training time is bit more then model #2 for given number of records and hence it is preferred to run these two models on larger set of data to choose perfect model for production use.